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Sample records for multi-objective evolutionary algorithm

  1. Convex hull ranking algorithm for multi-objective evolutionary algorithms

    NARCIS (Netherlands)

    Davoodi Monfrared, M.; Mohades, A.; Rezaei, J.

    2012-01-01

    Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity

  2. Multi-objective mixture-based iterated density estimation evolutionary algorithms

    NARCIS (Netherlands)

    Thierens, D.; Bosman, P.A.N.

    2001-01-01

    We propose an algorithm for multi-objective optimization using a mixture-based iterated density estimation evolutionary algorithm (MIDEA). The MIDEA algorithm is a prob- abilistic model building evolutionary algo- rithm that constructs at each generation a mixture of factorized probability

  3. Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

    Science.gov (United States)

    Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

    2014-03-01

    This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case

  4. Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

    Science.gov (United States)

    2009-03-10

    xfar by xint. Else, generate a new individual, using the Sobol pseudo- random sequence generator within the upper and lower bounds of the variables...12. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons. 2002. 13. Sobol , I. M., "Uniformly Distributed Sequences

  5. An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

    Directory of Open Access Journals (Sweden)

    Rafał Dreżewski

    2017-08-01

    Full Text Available Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

  6. Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Lvjiang Yin

    2016-12-01

    Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

  7. An Extensible Component-Based Multi-Objective Evolutionary Algorithm Framework

    DEFF Research Database (Denmark)

    Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

    2017-01-01

    The ability to easily modify the problem definition is currently missing in Multi-Objective Evolutionary Algorithms (MOEA). Existing MOEA frameworks do not support dynamic addition and extension of the problem formulation. The existing frameworks require a re-specification of the problem definition...

  8. Towards Automatic Controller Design using Multi-Objective Evolutionary Algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf

    of evolutionary computation, a choice was made to use multi-objective algorithms for the purpose of aiding in automatic controller design. More specifically, the choice was made to use the Non-dominated Sorting Genetic Algorithm II (NSGAII), which is one of the most potent algorithms currently in use...... for automatic controller design. However, because the field of evolutionary computation is relatively unknown in the field of control engineering, this thesis also includes a comprehensive introduction to the basic field of evolutionary computation as well as a description of how the field has previously been......In order to design the controllers of tomorrow, a need has risen for tools that can aid in the design of these. A desire to use evolutionary computation as a tool to achieve that goal is what gave inspiration for the work contained in this thesis. After having studied the foundations...

  9. Irrigation water allocation optimization using multi-objective evolutionary algorithm (MOEA) - a review

    Science.gov (United States)

    Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian

    2018-03-01

    This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.

  10. EFFICIENT MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR JOB SHOP SCHEDULING

    Institute of Scientific and Technical Information of China (English)

    Lei Deming; Wu Zhiming

    2005-01-01

    A new representation method is first presented based on priority rules. According to this method, each entry in the chromosome indicates that in the procedure of the Giffler and Thompson (GT) algorithm, the conflict occurring in the corresponding machine is resolved by the corresponding priority rule. Then crowding-measure multi-objective evolutionary algorithm (CMOEA) is designed,in which both archive maintenance and fitness assignment use crowding measure. Finally the comparisons between CMOEA and SPEA in solving 15 scheduling problems demonstrate that CMOEA is suitable to job shop scheduling.

  11. Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

    DEFF Research Database (Denmark)

    Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

    2015-01-01

    Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we...... compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...... of all aforementioned algorithms is assessed and compared using performance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical...

  12. Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Mengjun Ming

    2017-05-01

    Full Text Available Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes is maximized. To effectively solve this multi-objective problem (MOP, the multi-objective evolutionary algorithm based on decomposition (MOEA/D using localized penalty-based boundary intersection (LPBI method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker’s preference, the most satisfied configuration of the HRES can be identified.

  13. Low emittance lattice optimization using a multi-objective evolutionary algorithm

    International Nuclear Information System (INIS)

    Gao Weiwei; Wang Lin; Li Weimin; He Duohui

    2011-01-01

    A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure. (authors)

  14. Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems

    CERN Document Server

    2015-01-01

    This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field ...

  15. Scheduling for the National Hockey League Using a Multi-objective Evolutionary Algorithm

    Science.gov (United States)

    Craig, Sam; While, Lyndon; Barone, Luigi

    We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.

  16. Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System

    CERN Document Server

    Tydrichova, Magdalena

    2017-01-01

    In this project, various available multi-objective optimization evolutionary algorithms were compared considering their performance and distribution of solutions. The main goal was to select the most suitable algorithms for applications in cancer hadron therapy planning. For our purposes, a complex testing and analysis software was developed. Also, many conclusions and hypothesis have been done for the further research.

  17. An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, Taher [Electronic and Electrical Engineering Department, Shiraz University of Technology, Shiraz (Iran)

    2009-08-15

    This paper introduces a robust searching hybrid evolutionary algorithm to solve the multi-objective Distribution Feeder Reconfiguration (DFR). The main objective of the DFR is to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and balance the loads on the feeders. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. This paper presents a new approach based on norm3 for the DFR problem. In the proposed method, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. Since the proposed DFR is a multi objective and non-differentiable optimization problem, a new hybrid evolutionary algorithm (EA) based on the combination of the Honey Bee Mating Optimization (HBMO) and the Discrete Particle Swarm Optimization (DPSO), called DPSO-HBMO, is implied to solve it. The results of the proposed reconfiguration method are compared with the solutions obtained by other approaches, the original DPSO and HBMO over different distribution test systems. (author)

  18. Culture belief based multi-objective hybrid differential evolutionary algorithm in short term hydrothermal scheduling

    International Nuclear Information System (INIS)

    Zhang Huifeng; Zhou Jianzhong; Zhang Yongchuan; Lu Youlin; Wang Yongqiang

    2013-01-01

    Highlights: ► Culture belief is integrated into multi-objective differential evolution. ► Chaotic sequence is imported to improve evolutionary population diversity. ► The priority of convergence rate is proved in solving hydrothermal problem. ► The results show the quality and potential of proposed algorithm. - Abstract: A culture belief based multi-objective hybrid differential evolution (CB-MOHDE) is presented to solve short term hydrothermal optimal scheduling with economic emission (SHOSEE) problem. This problem is formulated for compromising thermal cost and emission issue while considering its complicated non-linear constraints with non-smooth and non-convex characteristics. The proposed algorithm integrates a modified multi-objective differential evolutionary algorithm into the computation model of culture algorithm (CA) as well as some communication protocols between population space and belief space, three knowledge structures in belief space are redefined according to these problem-solving characteristics, and in the differential evolution a chaotic factor is embedded into mutation operator for avoiding the premature convergence by enlarging the search scale when the search trajectory reaches local optima. Furthermore, a new heuristic constraint-handling technique is utilized to handle those complex equality and inequality constraints of SHOSEE problem. After the application on hydrothermal scheduling system, the efficiency and stability of the proposed CB-MOHDE is verified by its more desirable results in comparison to other method established recently, and the simulation results also reveal that CB-MOHDE can be a promising alternative for solving SHOSEE.

  19. Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

    Science.gov (United States)

    Wang, Chun; Ji, Zhicheng; Wang, Yan

    2017-07-01

    In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

  20. Multi-objective exergy-based optimization of a polygeneration energy system using an evolutionary algorithm

    International Nuclear Information System (INIS)

    Ahmadi, Pouria; Rosen, Marc A.; Dincer, Ibrahim

    2012-01-01

    A comprehensive thermodynamic modeling and optimization is reported of a polygeneration energy system for the simultaneous production of heating, cooling, electricity and hot water from a common energy source. This polygeneration system is composed of four major parts: gas turbine (GT) cycle, Rankine cycle, absorption cooling cycle and domestic hot water heater. A multi-objective optimization method based on an evolutionary algorithm is applied to determine the best design parameters for the system. The two objective functions utilized in the analysis are the total cost rate of the system, which is the cost associated with fuel, component purchasing and environmental impact, and the system exergy efficiency. The total cost rate of the system is minimized while the cycle exergy efficiency is maximized by using an evolutionary algorithm. To provide a deeper insight, the Pareto frontier is shown for multi-objective optimization. In addition, a closed form equation for the relationship between exergy efficiency and total cost rate is derived. Finally, a sensitivity analysis is performed to assess the effects of several design parameters on the system total exergy destruction rate, CO 2 emission and exergy efficiency.

  1. Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Min-Yin Liu

    2017-05-01

    Full Text Available Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz measured by electroencephalography (EEG mostly during non-rapid eye movement (NREM stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1 the lack of common benchmark databases, and (2 the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA, the Strength Pareto Evolutionary Algorithm (SPEA2, to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT, and two Hilbert-Huang transform (HHT based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

  2. Multi-objective optimization of HVAC system with an evolutionary computation algorithm

    International Nuclear Information System (INIS)

    Kusiak, Andrew; Tang, Fan; Xu, Guanglin

    2011-01-01

    A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables - supply air temperature and supply air duct static pressure set points - are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system. -- Highlights: → A data-mining approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system is presented. → The data used in the project has been collected from an experiment conducted at an energy research facility. → The approach presented in the paper leads to accomplishing significant energy savings without compromising the indoor air quality. → The energy savings are accomplished by computing set points for the supply air temperature and the supply air duct static pressure.

  3. A hybrid multi-objective evolutionary algorithm approach for ...

    Indian Academy of Sciences (India)

    V K MANUPATI

    for handling sequence- and machine-dependent set-up times ... algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventional ..... position and cognitive learning factor are considered for.

  4. Recent advances in evolutionary multi-objective optimization

    CERN Document Server

    Datta, Rituparna; Gupta, Abhishek

    2017-01-01

    This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high ...

  5. Optimization of externalities using DTM measures: a Pareto optimal multi objective optimization using the evolutionary algorithm SPEA2+

    NARCIS (Netherlands)

    Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, Michiel; Allkim, T.P.; van Arem, Bart

    2010-01-01

    Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto optimal set is determined by employing the SPEA2+ evolutionary algorithm.

  6. Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm

    International Nuclear Information System (INIS)

    Sayyaadi, Hoseyn; Amlashi, Emad Hadaddi; Amidpour, Majid

    2009-01-01

    Thermodynamic and thermoeconomic optimization of a vertical ground source heat pump system has been studied. A model based on the energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. The proposed vertical ground source heat pump system including eight decision variables is considered for optimization. An artificial intelligence technique known as evolutionary algorithm (EA) has been utilized as an optimization method. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective, thermoeconomic single objective and multi-objective optimizations are performed. In Multi-objective optimization, both thermodynamic and thermoeconomic objectives are considered, simultaneously. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from available optimal points on Pareto frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, to the annual number of operating hours and to the electricity cost are studied in detail.

  7. Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design

    International Nuclear Information System (INIS)

    Toffolo, A.; Lazzaretto, A.

    2002-01-01

    Thermoeconomic analyses in thermal system design are always focused on the economic objective. However, knowledge of only the economic minimum may not be sufficient in the decision making process, since solutions with a higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in energy market prices or in energy policies. This paper suggests how to perform a multi-objective optimization in order to find solutions that simultaneously satisfy exergetic and economic objectives. This corresponds to a search for the set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by an evolutionary algorithm, that features a new diversity preserving mechanism using as a test case the well-known CGAM problem. (author)

  8. An Evolutionary Approach for Bilevel Multi-objective Problems

    Science.gov (United States)

    Deb, Kalyanmoy; Sinha, Ankur

    Evolutionary multi-objective optimization (EMO) algorithms have been extensively applied to find multiple near Pareto-optimal solutions over the past 15 years or so. However, EMO algorithms for solving bilevel multi-objective optimization problems have not received adequate attention yet. These problems appear in many applications in practice and involve two levels, each comprising of multiple conflicting objectives. These problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem, thereby making them difficult to solve. In this paper, we discuss a recently proposed bilevel EMO procedure and show its working principle on a couple of test problems and on a business decision-making problem. This paper should motivate other EMO researchers to engage more into this important optimization task of practical importance.

  9. Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

    International Nuclear Information System (INIS)

    Martorell, S.; Carlos, S.; Villanueva, J.F.; Sanchez, A.I; Galvan, B.; Salazar, D.; Cepin, M.

    2006-01-01

    This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability

  10. Geomagnetic Navigation of Autonomous Underwater Vehicle Based on Multi-objective Evolutionary Algorithm.

    Science.gov (United States)

    Li, Hong; Liu, Mingyong; Zhang, Feihu

    2017-01-01

    This paper presents a multi-objective evolutionary algorithm of bio-inspired geomagnetic navigation for Autonomous Underwater Vehicle (AUV). Inspired by the biological navigation behavior, the solution was proposed without using a priori information, simply by magnetotaxis searching. However, the existence of the geomagnetic anomalies has significant influence on the geomagnetic navigation system, which often disrupts the distribution of the geomagnetic field. An extreme value region may easily appear in abnormal regions, which makes AUV lost in the navigation phase. This paper proposes an improved bio-inspired algorithm with behavior constraints, for sake of making AUV escape from the abnormal region. First, the navigation problem is considered as the optimization problem. Second, the environmental monitoring operator is introduced, to determine whether the algorithm falls into the geomagnetic anomaly region. Then, the behavior constraint operator is employed to get out of the abnormal region. Finally, the termination condition is triggered. Compared to the state-of- the-art, the proposed approach effectively overcomes the disturbance of the geomagnetic abnormal. The simulation result demonstrates the reliability and feasibility of the proposed approach in complex environments.

  11. A possibilistic approach to rotorcraft design through a multi-objective evolutionary algorithm

    Science.gov (United States)

    Chae, Han Gil

    Most of the engineering design processes in use today in the field may be considered as a series of successive decision making steps. The decision maker uses information at hand, determines the direction of the procedure, and generates information for the next step and/or other decision makers. However, the information is often incomplete, especially in the early stages of the design process of a complex system. As the complexity of the system increases, uncertainties eventually become unmanageable using traditional tools. In such a case, the tools and analysis values need to be "softened" to account for the designer's intuition. One of the methods that deals with issues of intuition and incompleteness is possibility theory. Through the use of possibility theory coupled with fuzzy inference, the uncertainties estimated by the intuition of the designer are quantified for design problems. By involving quantified uncertainties in the tools, the solutions can represent a possible set, instead of a crisp spot, for predefined levels of certainty. From a different point of view, it is a well known fact that engineering design is a multi-objective problem or a set of such problems. The decision maker aims to find satisfactory solutions, sometimes compromising the objectives that conflict with each other. Once the candidates of possible solutions are generated, a satisfactory solution can be found by various decision-making techniques. A number of multi-objective evolutionary algorithms (MOEAs) have been developed, and can be found in the literature, which are capable of generating alternative solutions and evaluating multiple sets of solutions in one single execution of an algorithm. One of the MOEA techniques that has been proven to be very successful for this class of problems is the strength Pareto evolutionary algorithm (SPEA) which falls under the dominance-based category of methods. The Pareto dominance that is used in SPEA, however, is not enough to account for the

  12. Multi-objective evolutionary optimisation for product design and manufacturing

    CERN Document Server

    2011-01-01

    Presents state-of-the-art research in the area of multi-objective evolutionary optimisation for integrated product design and manufacturing Provides a comprehensive review of the literature Gives in-depth descriptions of recently developed innovative and novel methodologies, algorithms and systems in the area of modelling, simulation and optimisation

  13. Thermo-economic multi-objective optimization of solar dish-Stirling engine by implementing evolutionary algorithm

    International Nuclear Information System (INIS)

    Ahmadi, Mohammad H.; Sayyaadi, Hoseyn; Mohammadi, Amir H.; Barranco-Jimenez, Marco A.

    2013-01-01

    Highlights: • Thermo-economic multi-objective optimization of solar dish-Stirling engine is studied. • Application of the evolutionary algorithm is investigated. • Error analysis is done to find out the error through investigation. - Abstract: In the recent years, remarkable attention is drawn to Stirling engine due to noticeable advantages, for instance a lot of resources such as biomass, fossil fuels and solar energy can be applied as heat source. Great number of studies are conducted on Stirling engine and finite time thermo-economic is one of them. In the present study, the dimensionless thermo-economic objective function, thermal efficiency and dimensionless power output are optimized for a dish-Stirling system using finite time thermo-economic analysis and NSGA-II algorithm. Optimized answers are chosen from the results using three decision-making methods. Error analysis is done to find out the error through investigation

  14. Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Boyang Qu

    2017-12-01

    Full Text Available The intermittency of wind power and the large-scale integration of electric vehicles (EVs bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

  15. ADAPTIVE SELECTION OF AUXILIARY OBJECTIVES IN MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS

    Directory of Open Access Journals (Sweden)

    I. A. Petrova

    2016-05-01

    Full Text Available Subject of Research.We propose to modify the EA+RL method, which increases efficiency of evolutionary algorithms by means of auxiliary objectives. The proposed modification is compared to the existing objective selection methods on the example of travelling salesman problem. Method. In the EA+RL method a reinforcement learning algorithm is used to select an objective – the target objective or one of the auxiliary objectives – at each iteration of the single-objective evolutionary algorithm.The proposed modification of the EA+RL method adopts this approach for the usage with a multiobjective evolutionary algorithm. As opposed to theEA+RL method, in this modification one of the auxiliary objectives is selected by reinforcement learning and optimized together with the target objective at each step of the multiobjective evolutionary algorithm. Main Results.The proposed modification of the EA+RL method was compared to the existing objective selection methods on the example of travelling salesman problem. In the EA+RL method and its proposed modification reinforcement learning algorithms for stationary and non-stationary environment were used. The proposed modification of the EA+RL method applied with reinforcement learning for non-stationary environment outperformed the considered objective selection algorithms on the most problem instances. Practical Significance. The proposed approach increases efficiency of evolutionary algorithms, which may be used for solving discrete NP-hard optimization problems. They are, in particular, combinatorial path search problems and scheduling problems.

  16. Comparing multi-objective non-evolutionary NLPQL and evolutionary genetic algorithm optimization of a DI diesel engine: DoE estimation and creating surrogate model

    International Nuclear Information System (INIS)

    Navid, Ali; Khalilarya, Shahram; Taghavifar, Hadi

    2016-01-01

    Highlights: • NLPQL algorithm with Latin hypercube and multi-objective GA were applied on engine. • NLPQL converge to the best solution at RunID41, MOGA introduces at RunID84. • Deeper, more encircled design gives the lowest NOx, greater radius and deeper bowl the highest IMEP. • The maximum IMEP and minimum ISFC obtained with NLPQL, the lowest NOx with MOGA. - Abstract: This study is concerned with the application of two major kinds of optimization algorithms on the baseline diesel engine in the class of evolutionary and non-evolutionary algorithms. The multi-objective genetic algorithm and non-linear programming by quadratic Lagrangian (NLPQL) method have completely different functions in optimizing and finding the global optimal design. The design variables are injection angle, half spray cone angle, inner distance of the bowl wall, and the bowl radius, while the objectives include NOx emission, spray droplet diameter, indicated mean effective pressure (IMEP), and indicated specific fuel consumption (ISFC). The restrictions were set on the objectives to distinguish between feasible designs and infeasible designs to sort those cases that cannot fulfill the demands of diesel engine designers and emission control measures. It is found that a design with deeper bowl and more encircled shape (higher swirl motion) is more suitable for NO_x emission control, whereas designs with a bigger bowl radius, and closer inner wall distance of the bowl (Di) may lead to higher engine efficiency indices. Moreover, it was revealed that the NLPQL could rapidly search for the best design at Run ID 41 compared to genetic algorithm, which is able to find the global optima at last runs (ID 84). Both techniques introduce almost the same geometrical shape of the combustion chamber with a negligible contrast in the injection system.

  17. Confronting Decision Cliffs: Diagnostic Assessment of Multi-Objective Evolutionary Algorithms' Performance for Addressing Uncertain Environmental Thresholds

    Science.gov (United States)

    Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.

    2014-12-01

    As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a

  18. Comparison of evolutionary computation algorithms for solving bi ...

    Indian Academy of Sciences (India)

    failure probability. Multiobjective Evolutionary Computation algorithms (MOEAs) are well-suited for Multiobjective task scheduling on heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic. Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with.

  19. An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

    Science.gov (United States)

    Deb, Kalyanmoy; Sinha, Ankur

    2010-01-01

    Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

  20. Study on hybrid multi-objective optimization algorithm for inverse treatment planning of radiation therapy

    International Nuclear Information System (INIS)

    Li Guoli; Song Gang; Wu Yican

    2007-01-01

    Inverse treatment planning for radiation therapy is a multi-objective optimization process. The hybrid multi-objective optimization algorithm is studied by combining the simulated annealing(SA) and genetic algorithm(GA). Test functions are used to analyze the efficiency of algorithms. The hybrid multi-objective optimization SA algorithm, which displacement is based on the evolutionary strategy of GA: crossover and mutation, is implemented in inverse planning of external beam radiation therapy by using two kinds of objective functions, namely the average dose distribution based and the hybrid dose-volume constraints based objective functions. The test calculations demonstrate that excellent converge speed can be achieved. (authors)

  1. A Note on Evolutionary Algorithms and Its Applications

    Science.gov (United States)

    Bhargava, Shifali

    2013-01-01

    This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.

  2. An improved fast and elitist multi-objective genetic algorithm-ANSGA-II for multi-objective optimization of inverse radiotherapy treatment planning

    International Nuclear Information System (INIS)

    Cao Ruifen; Li Guoli; Song Gang; Zhao Pan; Lin Hui; Wu Aidong; Huang Chenyu; Wu Yican

    2007-01-01

    Objective: To provide a fast and effective multi-objective optimization algorithm for inverse radiotherapy treatment planning system. Methods: Non-dominated Sorting Genetic Algorithm-NSGA-II is a representative of multi-objective evolutionary optimization algorithms and excels the others. The paper produces ANSGA-II that makes use of advantage of NSGA-II, and uses adaptive crossover and mutation to improve its flexibility; according the character of inverse radiotherapy treatment planning, the paper uses the pre-known knowledge to generate individuals of every generation in the course of optimization, which enhances the convergent speed and improves efficiency. Results: The example of optimizing average dose of a sheet of CT, including PTV, OAR, NT, proves the algorithm could find satisfied solutions in several minutes. Conclusions: The algorithm could provide clinic inverse radiotherapy treatment planning system with selection of optimization algorithms. (authors)

  3. Multi-Objective Optimization of the Hedging Model for reservoir Operation Using Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    sadegh sadeghitabas

    2015-12-01

    Full Text Available Multi-objective problems rarely ever provide a single optimal solution, rather they yield an optimal set of outputs (Pareto fronts. Solving these problems was previously accomplished by using some simplifier methods such as the weighting coefficient method used for converting a multi-objective problem to a single objective function. However, such robust tools as multi-objective meta-heuristic algorithms have been recently developed for solving these problems. The hedging model is one of the classic problems for reservoir operation that is generally employed for mitigating drought impacts in water resources management. According to this method, although it is possible to supply the total planned demands, only portions of the demands are met to save water by allowing small deficits in the current conditions in order to avoid or reduce severe deficits in future. The approach heavily depends on economic and social considerations. In the present study, the meta-heuristic algorithms of NSGA-II, MOPSO, SPEA-II, and AMALGAM are used toward the multi-objective optimization of the hedging model. For this purpose, the rationing factors involved in Taleghan dam operation are optimized over a 35-year statistical period of inflow. There are two objective functions: a minimizing the modified shortage index, and b maximizing the reliability index (i.e., two opposite objectives. The results show that the above algorithms are applicable to a wide range of optimal solutions. Among the algorithms, AMALGAM is found to produce a better Pareto front for the values of the objective function, indicating its more satisfactory performance.

  4. Design of a centrifugal compressor impeller using multi-objective optimization algorithm

    International Nuclear Information System (INIS)

    Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong; Choi, Jae Ho

    2009-01-01

    This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

  5. Design of a centrifugal compressor impeller using multi-objective optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of); Choi, Jae Ho [Samsung Techwin Co., Ltd., Changwon (Korea, Republic of)

    2009-07-01

    This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with {epsilon}-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

  6. Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Ranjan [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: ranjan.k@ks3.ecs.kyoto-u.ac.jp; Izui, Kazuhiro [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: izui@prec.kyoto-u.ac.jp; Yoshimura, Masataka [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: yoshimura@prec.kyoto-u.ac.jp; Nishiwaki, Shinji [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: shinji@prec.kyoto-u.ac.jp

    2009-04-15

    Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.

  7. Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

    International Nuclear Information System (INIS)

    Kumar, Ranjan; Izui, Kazuhiro; Yoshimura, Masataka; Nishiwaki, Shinji

    2009-01-01

    Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets

  8. Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

    DEFF Research Database (Denmark)

    Cao, Bin; Zhao, Jianwei; Yang, Po

    2018-01-01

    -objective evolutionary algorithms the Cooperative Coevolutionary Generalized Differential Evolution 3, the Cooperative Multi-objective Differential Evolution and the Nondominated Sorting Genetic Algorithm III, the proposed algorithm addresses the deployment optimization problem efficiently and effectively.......Using immune algorithms is generally a time-intensive process especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface...... (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large...

  9. Synthesizing multi-objective H2/H-infinity dynamic controller using evolutionary algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf; Langballe, A.S.; Wisniewski, Rafal

    This paper covers the design of an Evolutionary Algorithm (EA), which should be able to synthesize a mixed H2/H-infinity. It will be shown how a system can be expressed as Matrix Inequalities (MI) and these will then be used in the design of the EA. The main objective is to examine whether a mixed...... H2/H-infinity controller is feasible, and if so, how the optimal mixed controller might befound....

  10. Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria

    Science.gov (United States)

    Kowalczuk, Zdzisław; Białaszewski, Tomasz

    2018-01-01

    A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals and applied during parental crossover in the processes of evolutionary multi-objective optimization (EMOO). The article introduces the principles of the genetic-gender approach (GGA) and virtual gender approach (VGA), which are not just evolutionary techniques, but constitute a completely new rule (philosophy) for use in solving MOO tasks. The proposed approaches are validated against principal representatives of the EMOO algorithms of the state of the art in solving benchmark problems in the light of recognized EC performance criteria. The research shows the superiority of the gender approach in terms of effectiveness, reliability, transparency, intelligibility and MOO problem simplification, resulting in the great usefulness and practicability of GGA and VGA. Moreover, an important feature of GGA and VGA is that they alleviate the 'curse' of dimensionality typical of many engineering designs.

  11. Short-term economic environmental hydrothermal scheduling using improved multi-objective gravitational search algorithm

    International Nuclear Information System (INIS)

    Li, Chunlong; Zhou, Jianzhong; Lu, Peng; Wang, Chao

    2015-01-01

    Highlights: • Improved multi-objective gravitational search algorithm. • An elite archive set is proposed to guide evolutionary process. • Neighborhood searching mechanism to improve local search ability. • Adopt chaotic mutation for avoiding premature convergence. • Propose feasible space method to handle hydro plant constrains. - Abstract: With growing concerns about energy and environment, short-term economic environmental hydrothermal scheduling (SEEHS) plays a more and more important role in power system. Because of the two objectives and various constraints, SEEHS is a complex multi-objective optimization problem (MOOP). In order to solve the problem, we propose an improved multi-objective gravitational search algorithm (IMOGSA) in this paper. In IMOGSA, the mass of the agent is redefined by multiple objectives to make it suitable for MOOP. An elite archive set is proposed to keep Pareto optimal solutions and guide evolutionary process. For balancing exploration and exploitation, a neighborhood searching mechanism is presented to cooperate with chaotic mutation. Moreover, a novel method based on feasible space is proposed to handle hydro plant constraints during SEEHS, and a violation adjustment method is adopted to handle power balance constraint. For verifying its effectiveness, the proposed IMOGSA is applied to a hydrothermal system in two different case studies. The simulation results show that IMOGSA has a competitive performance in SEEHS when compared with other established algorithms

  12. Quantitative Trait Loci Mapping Problem: An Extinction-Based Multi-Objective Evolutionary Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Nicholas S. Flann

    2013-09-01

    Full Text Available The Quantitative Trait Loci (QTL mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS regression QTL models and considers the conflicting objectives of model simplicity versus model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.

  13. Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

    International Nuclear Information System (INIS)

    Salazar, Daniel; Rocco, Claudio M.; Galvan, Blas J.

    2006-01-01

    This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature

  14. Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Salazar, Daniel [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain) and Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: danielsalazaraponte@gmail.com; Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve; Galvan, Blas J. [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain)]. E-mail: bgalvan@step.es

    2006-09-15

    This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.

  15. Optimal operational strategies for a day-ahead electricity market in the presence of market power using multi-objective evolutionary algorithms

    Science.gov (United States)

    Rodrigo, Deepal

    2007-12-01

    This dissertation introduces a novel approach for optimally operating a day-ahead electricity market not only by economically dispatching the generation resources but also by minimizing the influences of market manipulation attempts by the individual generator-owning companies while ensuring that the power system constraints are not violated. Since economic operation of the market conflicts with the individual profit maximization tactics such as market manipulation by generator-owning companies, a methodology that is capable of simultaneously optimizing these two competing objectives has to be selected. Although numerous previous studies have been undertaken on the economic operation of day-ahead markets and other independent studies have been conducted on the mitigation of market power, the operation of a day-ahead electricity market considering these two conflicting objectives simultaneously has not been undertaken previously. These facts provided the incentive and the novelty for this study. A literature survey revealed that many of the traditional solution algorithms convert multi-objective functions into either a single-objective function using weighting schemas or undertake optimization of one function at a time. Hence, these approaches do not truly optimize the multi-objectives concurrently. Due to these inherent deficiencies of the traditional algorithms, the use of alternative non-traditional solution algorithms for such problems has become popular and widely used. Of these, multi-objective evolutionary algorithms (MOEA) have received wide acceptance due to their solution quality and robustness. In the present research, three distinct algorithms were considered: a non-dominated sorting genetic algorithm II (NSGA II), a multi-objective tabu search algorithm (MOTS) and a hybrid of multi-objective tabu search and genetic algorithm (MOTS/GA). The accuracy and quality of the results from these algorithms for applications similar to the problem investigated here

  16. Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

    Science.gov (United States)

    Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

    2010-01-01

    This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

  17. Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

    Science.gov (United States)

    Trianni, Vito; López-Ibáñez, Manuel

    2015-01-01

    The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

  18. Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

    Directory of Open Access Journals (Sweden)

    Vito Trianni

    Full Text Available The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled. However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

  19. Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms

    International Nuclear Information System (INIS)

    Atashkari, K.; Nariman-Zadeh, N.; Goelcue, M.; Khalkhali, A.; Jamali, A.

    2007-01-01

    The main reason for the efficiency decrease at part load conditions for four-stroke spark-ignition (SI) engines is the flow restriction at the cross-sectional area of the intake system. Traditionally, valve-timing has been designed to optimize operation at high engine-speed and wide open throttle conditions. Several investigations have demonstrated that improvements at part load conditions in engine performance can be accomplished if the valve-timing is variable. Controlling valve-timing can be used to improve the torque and power curve as well as to reduce fuel consumption and emissions. In this paper, a group method of data handling (GMDH) type neural network and evolutionary algorithms (EAs) are firstly used for modelling the effects of intake valve-timing (V t ) and engine speed (N) of a spark-ignition engine on both developed engine torque (T) and fuel consumption (Fc) using some experimentally obtained training and test data. Using such obtained polynomial neural network models, a multi-objective EA (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto based optimization of the variable valve-timing engine considering two conflicting objectives such as torque (T) and fuel consumption (Fc). The comparison results demonstrate the superiority of the GMDH type models over feedforward neural network models in terms of the statistical measures in the training data, testing data and the number of hidden neurons. Further, it is shown that some interesting and important relationships, as useful optimal design principles, involved in the performance of the variable valve-timing four-stroke spark-ignition engine can be discovered by the Pareto based multi-objective optimization of the polynomial models. Such important optimal principles would not have been obtained without the use of both the GMDH type neural network modelling and the multi-objective Pareto optimization approach

  20. A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2014-01-01

    Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

  1. Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

    Directory of Open Access Journals (Sweden)

    Rajesh Kumar

    2016-06-01

    Full Text Available Brayton heat engine model is developed in MATLAB simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. The proposed work investigates optimal values of various decision variables that simultaneously optimize power output, thermal efficiency and ecological function using evolutionary algorithm based on NSGA-II. Pareto optimal frontier between triple and dual objectives is obtained and best optimal value is selected using Fuzzy, TOPSIS, LINMAP and Shannon’s entropy decision making methods. Triple objective evolutionary approach applied to the proposed model gives power output, thermal efficiency, ecological function as (53.89 kW, 0.1611, −142 kW which are 29.78%, 25.86% and 21.13% lower in comparison with reversible system. Furthermore, the present study reflects the effect of various heat capacitance rates and component efficiencies on triple objectives in graphical custom. Finally, with the aim of error investigation, average and maximum errors of obtained results are computed.

  2. Computational characterization of HPGe detectors usable for a wide variety of source geometries by using Monte Carlo simulation and a multi-objective evolutionary algorithm

    Science.gov (United States)

    Guerra, J. G.; Rubiano, J. G.; Winter, G.; Guerra, A. G.; Alonso, H.; Arnedo, M. A.; Tejera, A.; Martel, P.; Bolivar, J. P.

    2017-06-01

    In this work, we have developed a computational methodology for characterizing HPGe detectors by implementing in parallel a multi-objective evolutionary algorithm, together with a Monte Carlo simulation code. The evolutionary algorithm is used for searching the geometrical parameters of a model of detector by minimizing the differences between the efficiencies calculated by Monte Carlo simulation and two reference sets of Full Energy Peak Efficiencies (FEPEs) corresponding to two given sample geometries, a beaker of small diameter laid over the detector window and a beaker of large capacity which wrap the detector. This methodology is a generalization of a previously published work, which was limited to beakers placed over the window of the detector with a diameter equal or smaller than the crystal diameter, so that the crystal mount cap (which surround the lateral surface of the crystal), was not considered in the detector model. The generalization has been accomplished not only by including such a mount cap in the model, but also using multi-objective optimization instead of mono-objective, with the aim of building a model sufficiently accurate for a wider variety of beakers commonly used for the measurement of environmental samples by gamma spectrometry, like for instance, Marinellis, Petris, or any other beaker with a diameter larger than the crystal diameter, for which part of the detected radiation have to pass through the mount cap. The proposed methodology has been applied to an HPGe XtRa detector, providing a model of detector which has been successfully verificated for different source-detector geometries and materials and experimentally validated using CRMs.

  3. Food processing optimization using evolutionary algorithms | Enitan ...

    African Journals Online (AJOL)

    Evolutionary algorithms are widely used in single and multi-objective optimization. They are easy to use and provide solution(s) in one simulation run. They are used in food processing industries for decision making. Food processing presents constrained and unconstrained optimization problems. This paper reviews the ...

  4. Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

    OpenAIRE

    Rajesh Kumar; S.C. Kaushik; Raj Kumar; Ranjana Hans

    2016-01-01

    Brayton heat engine model is developed in MATLAB simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. The proposed work investigates optimal values of various decision variables that simultaneously optimize power output, thermal efficiency and ecological function using evolutionary algorithm based on NSGA-II. Pareto optimal frontier between triple and dual objectives is obtained and best optimal value is s...

  5. Multi-objective optimization with estimation of distribution algorithm in a noisy environment.

    Science.gov (United States)

    Shim, Vui Ann; Tan, Kay Chen; Chia, Jun Yong; Al Mamun, Abdullah

    2013-01-01

    Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

  6. Algorithmic Mechanism Design of Evolutionary Computation.

    Science.gov (United States)

    Pei, Yan

    2015-01-01

    We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

  7. A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy

    International Nuclear Information System (INIS)

    Lahanas, M; Baltas, D; Zamboglou, N

    2003-01-01

    Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives

  8. Mono and multi-objective optimization techniques applied to a large range of industrial test cases using Metamodel assisted Evolutionary Algorithms

    Science.gov (United States)

    Fourment, Lionel; Ducloux, Richard; Marie, Stéphane; Ejday, Mohsen; Monnereau, Dominique; Massé, Thomas; Montmitonnet, Pierre

    2010-06-01

    The use of material processing numerical simulation allows a strategy of trial and error to improve virtual processes without incurring material costs or interrupting production and therefore save a lot of money, but it requires user time to analyze the results, adjust the operating conditions and restart the simulation. Automatic optimization is the perfect complement to simulation. Evolutionary Algorithm coupled with metamodelling makes it possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. Ten industrial partners have been selected to cover the different area of the mechanical forging industry and provide different examples of the forming simulation tools. It aims to demonstrate that it is possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. The large computational time is handled by a metamodel approach. It allows interpolating the objective function on the entire parameter space by only knowing the exact function values at a reduced number of "master points". Two algorithms are used: an evolution strategy combined with a Kriging metamodel and a genetic algorithm combined with a Meshless Finite Difference Method. The later approach is extended to multi-objective optimization. The set of solutions, which corresponds to the best possible compromises between the different objectives, is then computed in the same way. The population based approach allows using the parallel capabilities of the utilized computer with a high efficiency. An optimization module, fully embedded within the Forge2009 IHM, makes possible to cover all the defined examples, and the use of new multi-core hardware to compute several simulations at the same time reduces the needed time dramatically. The presented examples

  9. Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

    Directory of Open Access Journals (Sweden)

    Feng Lin

    2007-11-01

    Full Text Available Abstract Background Peptides binding to Major Histocompatibility Complex (MHC class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides. In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs. Results The proposed methods are intended for finding peptides binding to MHC class II I-Ag7 molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-Ag7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1 an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2 quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules. Conclusion We present two MOEA-based algorithms for finding motifs

  10. Evolutionary optimization and game strategies for advanced multi-disciplinary design applications to aeronautics and UAV design

    CERN Document Server

    Periaux, Jacques; Lee, Dong Seop Chris

    2015-01-01

    Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named “Hybridized Nash-Pareto games”. Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with c...

  11. A proposal of multi-objective function for submarine rigid pipelines route optimization via evolutionary algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Fernandes, D.H.; Medeiros, A.R. [Subsea7, Niteroi, RJ (Brazil); Jacob, B.P.; Lima, B.S.L.P.; Albrecht, C.H. [Universidade Federaldo Rio de Janeiro (COPPE/UFRJ), RJ (Brazil). Coordenacao de Programas de Pos-graduacao em Engenharia

    2009-07-01

    This work presents studies regarding the determination of optimal pipeline routes for offshore applications. The assembly of an objective function is presented; this function can be later associated with Evolutionary Algorithm to implement a computational tool for the automatic determination of the most advantageous pipeline route for a given scenario. This tool may reduce computational overheads, avoid mistakes with route interpretation, and minimize costs with respect to submarine pipeline design and installation. The following aspects can be considered in the assembly of the objective function: Geophysical and geotechnical data obtained from the bathymetry and sonography; the influence of the installation method, total pipeline length and number of free spans to be mitigated along the routes as well as vessel time for both cases. Case studies are presented to illustrate the use of the proposed objective function, including a sensitivity analysis intended to identify the relative influence of selected parameters in the evaluation of different routes. (author)

  12. A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

    Institute of Scientific and Technical Information of China (English)

    Su YAN; Kaiquan CAI

    2017-01-01

    Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.

  13. A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

    Directory of Open Access Journals (Sweden)

    Su YAN

    2017-06-01

    Full Text Available Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO, the network-wide 4D flight trajectories planning (N4DFTP problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs (3D position and time for all the flights in the whole airway network. Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity, an efficient model for strategic-level conflict management is developed in this paper. Specifically, a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated. In consideration of the large-scale, high-complexity, and multi-objective characteristics of the N4DFTP problem, a multi-objective multi-memetic algorithm (MOMMA that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented. It is capable of rapidly and effectively allocating 4DTs via rerouting, target time controlling, and flight level changing. Additionally, to balance the ability of exploitation and exploration of the algorithm, a special hybridization scheme is adopted for the integration of local and global search. Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4DFTP problem. The solutions achieved are competitive for elaborate decision support under a TBO environment.

  14. Development of antibiotic regimens using graph based evolutionary algorithms.

    Science.gov (United States)

    Corns, Steven M; Ashlock, Daniel A; Bryden, Kenneth M

    2013-12-01

    This paper examines the use of evolutionary algorithms in the development of antibiotic regimens given to production animals. A model is constructed that combines the lifespan of the animal and the bacteria living in the animal's gastro-intestinal tract from the early finishing stage until the animal reaches market weight. This model is used as the fitness evaluation for a set of graph based evolutionary algorithms to assess the impact of diversity control on the evolving antibiotic regimens. The graph based evolutionary algorithms have two objectives: to find an antibiotic treatment regimen that maintains the weight gain and health benefits of antibiotic use and to reduce the risk of spreading antibiotic resistant bacteria. This study examines different regimens of tylosin phosphate use on bacteria populations divided into Gram positive and Gram negative types, with a focus on Campylobacter spp. Treatment regimens were found that provided decreased antibiotic resistance relative to conventional methods while providing nearly the same benefits as conventional antibiotic regimes. By using a graph to control the information flow in the evolutionary algorithm, a variety of solutions along the Pareto front can be found automatically for this and other multi-objective problems. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  15. Multi-objective evolutionary emergency response optimization for major accidents

    International Nuclear Information System (INIS)

    Georgiadou, Paraskevi S.; Papazoglou, Ioannis A.; Kiranoudis, Chris T.; Markatos, Nikolaos C.

    2010-01-01

    Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area; protection of the population in buildings; and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues.

  16. A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, Taher; Meymand, Hamed Zeinoddini; Mojarrad, Hasan Doagou [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of)

    2011-05-15

    In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem. (author)

  17. Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

    Science.gov (United States)

    2010-03-01

    David A. Van Veldhuizen . Evo- lutionary Algorithms for Solving Multi-Objective Problems. Springer, New York, NY, 2nd edition, 2007. [9] Dean, Thomas...J.I. van Hemert, E. Marchiori, and A. G. Steenbeek. “Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive

  18. Computational characterization of HPGe detectors usable for a wide variety of source geometries by using Monte Carlo simulation and a multi-objective evolutionary algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Guerra, J.G., E-mail: jglezg2002@gmail.es [Departamento de Física, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria (Spain); Rubiano, J.G. [Departamento de Física, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria (Spain); Instituto Universitario de Estudios Ambientales y Recursos Naturales, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria (Spain); Winter, G. [Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en la Ingeniería, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria (Spain); Guerra, A.G.; Alonso, H.; Arnedo, M.A.; Tejera, A.; Martel, P. [Departamento de Física, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria (Spain); Instituto Universitario de Estudios Ambientales y Recursos Naturales, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria (Spain); Bolivar, J.P. [Departamento de Física Aplicada, Universidad de Huelva, 21071 Huelva (Spain)

    2017-06-21

    In this work, we have developed a computational methodology for characterizing HPGe detectors by implementing in parallel a multi-objective evolutionary algorithm, together with a Monte Carlo simulation code. The evolutionary algorithm is used for searching the geometrical parameters of a model of detector by minimizing the differences between the efficiencies calculated by Monte Carlo simulation and two reference sets of Full Energy Peak Efficiencies (FEPEs) corresponding to two given sample geometries, a beaker of small diameter laid over the detector window and a beaker of large capacity which wrap the detector. This methodology is a generalization of a previously published work, which was limited to beakers placed over the window of the detector with a diameter equal or smaller than the crystal diameter, so that the crystal mount cap (which surround the lateral surface of the crystal), was not considered in the detector model. The generalization has been accomplished not only by including such a mount cap in the model, but also using multi-objective optimization instead of mono-objective, with the aim of building a model sufficiently accurate for a wider variety of beakers commonly used for the measurement of environmental samples by gamma spectrometry, like for instance, Marinellis, Petris, or any other beaker with a diameter larger than the crystal diameter, for which part of the detected radiation have to pass through the mount cap. The proposed methodology has been applied to an HPGe XtRa detector, providing a model of detector which has been successfully verificated for different source-detector geometries and materials and experimentally validated using CRMs. - Highlights: • A computational method for characterizing HPGe detectors has been generalized. • The new version is usable for a wider range of sample geometries. • It starts from reference FEPEs obtained through a standard calibration procedure. • A model of an HPGe XtRa detector has been

  19. Computational characterization of HPGe detectors usable for a wide variety of source geometries by using Monte Carlo simulation and a multi-objective evolutionary algorithm

    International Nuclear Information System (INIS)

    Guerra, J.G.; Rubiano, J.G.; Winter, G.; Guerra, A.G.; Alonso, H.; Arnedo, M.A.; Tejera, A.; Martel, P.; Bolivar, J.P.

    2017-01-01

    In this work, we have developed a computational methodology for characterizing HPGe detectors by implementing in parallel a multi-objective evolutionary algorithm, together with a Monte Carlo simulation code. The evolutionary algorithm is used for searching the geometrical parameters of a model of detector by minimizing the differences between the efficiencies calculated by Monte Carlo simulation and two reference sets of Full Energy Peak Efficiencies (FEPEs) corresponding to two given sample geometries, a beaker of small diameter laid over the detector window and a beaker of large capacity which wrap the detector. This methodology is a generalization of a previously published work, which was limited to beakers placed over the window of the detector with a diameter equal or smaller than the crystal diameter, so that the crystal mount cap (which surround the lateral surface of the crystal), was not considered in the detector model. The generalization has been accomplished not only by including such a mount cap in the model, but also using multi-objective optimization instead of mono-objective, with the aim of building a model sufficiently accurate for a wider variety of beakers commonly used for the measurement of environmental samples by gamma spectrometry, like for instance, Marinellis, Petris, or any other beaker with a diameter larger than the crystal diameter, for which part of the detected radiation have to pass through the mount cap. The proposed methodology has been applied to an HPGe XtRa detector, providing a model of detector which has been successfully verificated for different source-detector geometries and materials and experimentally validated using CRMs. - Highlights: • A computational method for characterizing HPGe detectors has been generalized. • The new version is usable for a wider range of sample geometries. • It starts from reference FEPEs obtained through a standard calibration procedure. • A model of an HPGe XtRa detector has been

  20. Development of a multi-objective PBIL evolutionary algorithm applied to a nuclear reactor core reload optimization problem

    International Nuclear Information System (INIS)

    Machado, Marcelo D.; Dchirru, Roberto

    2005-01-01

    The nuclear reactor core reload optimization problem consists in finding a pattern of partially burned-up and fresh fuels that optimizes the plant's next operation cycle. This optimization problem has been traditionally solved using an expert's knowledge, but recently artificial intelligence techniques have also been applied successfully. The artificial intelligence optimization techniques generally have a single objective. However, most real-world engineering problems, including nuclear core reload optimization, have more than one objective (multi-objective) and these objectives are usually conflicting. The aim of this work is to develop a tool to solve multi-objective problems based on the Population-Based Incremental Learning (PBIL) algorithm. The new tool is applied to solve the Angra 1 PWR core reload optimization problem with the purpose of creating a Pareto surface, so that a pattern selected from this surface can be applied for the plant's next operation cycle. (author)

  1. The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

    Science.gov (United States)

    2013-06-01

    144 [38] Coello, Carlos A. Coello, Gary B Lamont, and David A Van Veldhuizen . Evolutionary Algorithms for Solving Multi-Objective Problems. Springer...Traveling Repairperson Problem (DTRP) Policies Proposed by Bertsimas and Van Ryzin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3...queuing theory perspective. Table 3.2: DTRP Policies Proposed by Bertsimas and Van Ryzin. Name Description First Come First Serve (FCFS) vehicles

  2. Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

    DEFF Research Database (Denmark)

    Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

    2013-01-01

    This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

  3. Environmental/economic dispatch problem of power system by using an enhanced multi-objective differential evolution algorithm

    International Nuclear Information System (INIS)

    Lu Youlin; Zhou Jianzhong; Qin Hui; Wang Ying; Zhang Yongchuan

    2011-01-01

    An enhanced multi-objective differential evolution algorithm (EMODE) is proposed in this paper to solve environmental/economic dispatch (EED) problem by considering the minimal of fuel cost and emission effects synthetically. In the proposed algorithm, an elitist archive technique is adopted to retain the non-dominated solutions obtained during the evolutionary process, and the operators of DE are modified according to the characteristics of multi-objective optimization problems. Moreover, in order to avoid premature convergence, a local random search (LRS) operator is integrated with the proposed method to improve the convergence performance. In view of the difficulties of handling the complicated constraints of EED problem, a new heuristic constraints handling method without any penalty factor settings is presented. The feasibility and effectiveness of the proposed EMODE method is demonstrated for a test power system. Compared with other methods, EMODE can get higher quality solutions by reducing the fuel cost and the emission effects synthetically.

  4. Multi-objective optimization using genetic algorithms: A tutorial

    International Nuclear Information System (INIS)

    Konak, Abdullah; Coit, David W.; Smith, Alice E.

    2006-01-01

    Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity

  5. TÉCNICAS EVOLUTIVAS EN PROBLEMAS MULTI-OBJETIVOS EN EL PROCESO DE PLANIFICACIÓN DE LA PRODUCCIÓN / EVOLUTIONARY TECHNIQUES FOR MULTI-OBJECTIVE PROBLEMS IN PRODUCTION PLANNING

    Directory of Open Access Journals (Sweden)

    Mariano Frutos-Alazard

    2012-01-01

    Full Text Available La planificación, en el ámbito productivo, se encarga de diseñar, coordinar, administrar y controlar todas las operaciones que se hallan presentes en la explotación de los sistemas productivos. En este marco de trabajo, aparecen numerosos Problemas de Optimización Multi-objetivo (MOPs. Éstos constan de varias funciones que suelen ser complejas y evaluarlas puede ser muy costoso. La optimización multi-objetivo es la disciplina que trata de encontrar las soluciones, denominadas Pareto óptimas, a este tipo de problemas. La compleja resolución de los MOPs es debida a las dimensiones propias del problema, al carácter combinatorio de los algoritmos y a la naturaleza de los objetivos, los cuales están vinculados a la eficiencia del sistema. En las últimas décadas muchos MOPs vinculados a la producción han sido tratados con éxito con técnicas de resolución basadas en Algoritmos Genéticos. En este trabajo se evalúa a NSGAII (Non-dominated Sorting Genetic Algorithm II, SPEAII (Strength Pareto Evolutionary Algorithm II y a sus antecesores, NSGA y SPEA, en el proceso de planificación de la producción no estandarizada. Luego de la experiencia realizada, el algoritmo NSGAII mostró mayor eficiencia.Planning in production environments takes care of designing, coordinating, managing and controlling all the operations existing in the use of productive systems. There are, in the framework analyzed within this work, several relevant Multi-Objective Optimization Problems (MOPs. They consist of several functions which tend to be complex and expensive to evaluate. Multi-objective optimization is the discipline developed to provide solutions, called Pareto optimal, for the simultaneous optimization of those functions. The costs of solving MOPs is due to the dimension of the problems, the combinatorial nature of the algorithms and the kind of objectives represented, linked to the efficiency of the system.. In the last decades several production

  6. Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

    Science.gov (United States)

    Zhang, Jian; Gan, Yang

    2018-04-01

    The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

  7. Hybrid Multi-objective Forecasting of Solar Photovoltaic Output Using Kalman Filter based Interval Type-2 Fuzzy Logic System

    DEFF Research Database (Denmark)

    Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin

    2017-01-01

    Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic...... system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA...

  8. Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

    Science.gov (United States)

    Guo, Zhan; Yan, Xuefeng

    2018-04-01

    Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

  9. Multi-objective optimal power flow with FACTS devices

    International Nuclear Information System (INIS)

    Basu, M.

    2011-01-01

    This paper presents multi-objective differential evolution to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FACTS devices considered include thyristor controlled series capacitor (TCSC) and thyristor controlled phase shifter (TCPS). The proposed approach has been examined and tested on the modified IEEE 30-bus and 57-bus test systems. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, strength pareto evolutionary algorithm 2 and pareto differential evolution.

  10. Analysing the performance of dynamic multi-objective optimisation algorithms

    CSIR Research Space (South Africa)

    Helbig, M

    2013-06-01

    Full Text Available and the goal of the algorithm is to track a set of tradeoff solutions over time. Analysing the performance of a dynamic multi-objective optimisation algorithm (DMOA) is not a trivial task. For each environment (before a change occurs) the DMOA has to find a set...

  11. Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms

    International Nuclear Information System (INIS)

    Tahani, Mojtaba; Babayan, Narek; Astaraei, Fatemeh Razi; Moghadam, Ali

    2015-01-01

    Highlights: • The performance of four different Meta heuristic optimization algorithms was studied. • Power coefficient and produced torque on stationary blade were selected as objective functions. • Chord and twist distributions were selected as decision variables. • All optimization algorithms were combined with blade element momentum theory. • The best Pareto front was obtained by multi objective flower pollination algorithm for HATCTs. - Abstract: The performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry. According to this fact, the optimum performance will be achieved by optimized geometry. In this research study, the multi objective optimization of the HATCT is carried out by using four different multi objective optimization algorithms and their performance is evaluated in combination with blade element momentum theory (BEM). The second version of non-dominated sorting genetic algorithm (NSGA-II), multi objective particle swarm optimization algorithm (MOPSO), multi objective cuckoo search algorithm (MOCS) and multi objective flower pollination algorithm (MOFPA) are the selected algorithms. The power coefficient and the produced torque on stationary blade are selected as objective functions and chord and twist distributions along the blade span are selected as decision variables. These algorithms are combined with the blade element momentum (BEM) theory for the purpose of achieving the best Pareto front. The obtained Pareto fronts are compared with each other. Different sets of experiments are carried out by considering different numbers of iterations, population size and tip speed ratios. The Pareto fronts which are achieved by MOFPA and NSGA-II have better quality in comparison to MOCS and MOPSO, but on the other hand a detail comparison between the first fronts of MOFPA and NSGA-II indicated that MOFPA algorithm can obtain the best Pareto front and can maximize the power coefficient up to 4.3% and the

  12. Improved multi-objective clustering algorithm using particle swarm optimization.

    Science.gov (United States)

    Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang

    2017-01-01

    Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

  13. EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

    Science.gov (United States)

    Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

    2015-01-01

    Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

  14. An experimental analysis of design choices of multi-objective ant colony optimization algorithms

    OpenAIRE

    Lopez-Ibanez, Manuel; Stutzle, Thomas

    2012-01-01

    There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuris...

  15. Improved multi-objective clustering algorithm using particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Congcong Gong

    Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

  16. A Multi-objective Optimization Application in Friction Stir Welding: Considering Thermo-mechanical Aspects

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Hattel, Jesper Henri

    2010-01-01

    speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermomechanical...

  17. Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

    Science.gov (United States)

    Lopez, Nicolas

    This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

  18. Multi-objective trajectory optimization of Space Manoeuvre Vehicle using adaptive differential evolution and modified game theory

    Science.gov (United States)

    Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun

    2017-07-01

    Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.

  19. Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

    Science.gov (United States)

    Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

    2017-02-01

    In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

  20. Multi-objective design of PV-wind-diesel-hydrogen-battery systems

    Energy Technology Data Exchange (ETDEWEB)

    Dufo-Lopez, Rodolfo; Bernal-Agustin, Jose L. [Department of Electrical Engineering, University of Zaragoza, Calle Maria de Luna 3, 50018-Zaragoza (Spain)

    2008-12-15

    This paper presents, for the first time, a triple multi-objective design of isolated hybrid systems minimizing, simultaneously, the total cost throughout the useful life of the installation, pollutant emissions (CO{sub 2}) and unmet load. For this task, a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combination of components of the hybrid system and control strategies. As an example of application, a complex PV-wind-diesel-hydrogen-battery system has been designed, obtaining a set of possible solutions (Pareto Set). The results achieved demonstrate the practical utility of the developed design method. (author)

  1. An efficient non-dominated sorting method for evolutionary algorithms.

    Science.gov (United States)

    Fang, Hongbing; Wang, Qian; Tu, Yi-Cheng; Horstemeyer, Mark F

    2008-01-01

    We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

  2. Optimization of a novel carbon dioxide cogeneration system using artificial neural network and multi-objective genetic algorithm

    International Nuclear Information System (INIS)

    Jamali, Arash; Ahmadi, Pouria; Mohd Jaafar, Mohammad Nazri

    2014-01-01

    In this research study, a combined cycle based on the Brayton power cycle and the ejector expansion refrigeration cycle is proposed. The proposed cycle can provide heating, cooling and power simultaneously. One of the benefits of such a system is to be driven by low temperature heat sources and using CO 2 as working fluid. In order to enhance the understanding of the current work, a comprehensive parametric study and exergy analysis are conducted to determine the effects of the thermodynamic parameters on the system performance and the exergy destruction rate in the components. The suggested cycle can save the energy around 46% in comparison with a system producing cooling, power and hot water separately. On the other hand, to optimize a system to meet the load requirement, the surface area of the heat exchangers is determined and optimized. The results of this section can be used when a compact system is also an objective function. Along with a comprehensive parametric study and exergy analysis, a complete optimization study is carried out using a multi-objective evolutionary based genetic algorithm considering two different objective functions, heat exchangers size (to be minimized) and exergy efficiency (to be maximized). The Pareto front of the optimization problem and a correlation between exergy efficiency and total heat exchangers length is presented in order to predict the trend of optimized points. The suggested system can be a promising combined system for buildings and outland regions. - Highlights: •Energy and exergy analysis of a novel CHP system are reported. •A comprehensive parametric study is conducted to enhance the understanding of the system performance. •Apply a multi-objective optimization technique based on a code developed in the Matlab software program using an evolutionary algorithm

  3. Safety management in NPPs using evolutionary algorithm

    International Nuclear Information System (INIS)

    Mishra, A.; Patwardhan, A.; Chauhan, A.; Verma, A.K.

    2005-01-01

    Technical specification and maintenance (TS and M) activities in a plant are associated with controlling risk or with satisfying requirements, and are candidates to be evaluated for their resource effectiveness in risk-informed applications. The general goal of safety management in Nuclear Power Plants (NPPs) is to make requirements and activities more risk effective and less costly. Accordingly, the risk-based analysis of Technical Specification (RBTS) is being considered in evaluating current TS. The multi objective optimization of the TS and M requirements of a NPP based on risk and cost, gives the pareto-optimal solutions, from which the utility can pick its decision variables suiting its interest. In this paper a multi objective Evolutionary Algorithm technique has been used to make a trade-off between risk and cost both at the system level and at the plant level for Loss of coolant Accident (LOCA) and Main Steam Line Break (MSLB) as initiating events. (authors)

  4. A Multi Agent System for Flow-Based Intrusion Detection Using Reputation and Evolutionary Computation

    Science.gov (United States)

    2011-03-01

    pertinent example of the application of Evolutionary Algorithms to pattern recognition comes from Radtke et al. [130]. The authors apply Multi- Objective...J., T. Zseby, and B. Claise. S. Zander,” Requirements for IP Flow Information Export (IPFIX). Technical report, RFC 3917, October 2004. [130] Radtke ...hal.inria.fr/inria-00104200/en/. [131] Radtke , P.V.W., T. Wong, and R. Sabourin. “A multi-objective memetic al- gorithm for intelligent feature extraction

  5. Multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm

    International Nuclear Information System (INIS)

    Wang, Jiangfeng; Yan, Zhequan; Wang, Man; Li, Maoqing; Dai, Yiping

    2013-01-01

    Highlights: • Multi-objective optimization of an ORC is conducted to obtain optimum performance. • NSGA-II is employed to solve this multi-objective optimization problem. • The effects of parameters on the exergy efficiency and capital cost are examined. - Abstract: Organic Rankine cycle (ORC) can effectively recover low grade waste heat due to its excellent thermodynamic performance. Based on the examinations of the effects of key thermodynamic parameters on the exergy efficiency and overall capital cost, multi-objective optimization of the ORC with R134a as working fluid is conducted to achieve the system optimization design from both thermodynamic and economic aspects using Non-dominated sorting genetic algorithm-II (NSGA-II). The exergy efficiency and overall capital cost are selected as two objective functions to maximize the exergy efficiency and minimize the overall capital cost under the given waste heat conditions. Turbine inlet pressure, turbine inlet temperature, pinch temperature difference, approach temperature difference and condenser temperature difference are selected as the decision variables owing to their significant effects on the exergy efficiency and overall capital cost. A Pareto frontier obtained shows that an increase in the exergy efficiency can increase the overall capital cost of the ORC system. The optimum design solution with their corresponding decision variables is selected from the Pareto frontier. The optimum exergy efficiency and overall capital cost are 13.98% and 129.28 × 10 4 USD, respectively, under the given waste heat conditions

  6. Multi-objective Optimization of Process Parameters in Friction Stir Welding

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Hattel, Jesper Henri

    The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational...... speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2- dimensional sequentially coupled thermo...

  7. An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

    Directory of Open Access Journals (Sweden)

    Jiuyuan Huo

    2017-02-01

    Full Text Available To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

  8. Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Najafi, Hamidreza; Najafi, Behzad [K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran (Iran)

    2010-06-15

    In the present paper, a plate and frame heat exchanger is considered. Multi-objective optimization using genetic algorithm is developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Vividly, considered objective functions are conflicting and no single solution can satisfy both objectives simultaneously. Multi-objective optimization procedure yields a set of optimal solutions, called Pareto front, each of which is a trade-off between objectives and can be selected by the user, regarding the application and the project's limits. The presented work takes care of numerous geometric parameters in the presence of logical constraints. A sensitivity analysis is also carried out to study the effects of different geometric parameters on the considered objective functions. Modeling the system and implementing the multi-objective optimization via genetic algorithm has been performed by MATLAB. (orig.)

  9. Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

    Science.gov (United States)

    Smith, Robert W; van Sluijs, Bob; Fleck, Christian

    2017-12-02

    Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

  10. Implementation of an evolutionary algorithm in planning investment in a power distribution system

    Directory of Open Access Journals (Sweden)

    Carlos Andrés García Montoya

    2011-06-01

    Full Text Available The definition of an investment plan to implement in a distribution power system, is a task that constantly faced by utilities. This work presents a methodology for determining the investment plan for a distribution power system under a shortterm, using as a criterion for evaluating investment projects, associated costs and customers benefit from its implementation. Given the number of projects carried out annually on the system, the definition of an investment plan requires the use of computational tools to evaluate, a set of possibilities, the one that best suits the needs of the present system and better results. That is why in the job, implementing a multi objective evolutionary algorithm SPEA (Strength Pareto Evolutionary Algorithm, which, based on the principles of Pareto optimality, it deliver to the planning expert, the best solutions found in the optimization process. The performance of the algorithm is tested using a set of projects to determine the best among the possible plans. We analyze also the effect of operators on the performance of evolutionary algorithm and results.

  11. Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm

    Directory of Open Access Journals (Sweden)

    Carolina Lagos

    2014-01-01

    Full Text Available Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP, the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric.

  12. Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Qin Hui [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Zhou Jianzhong, E-mail: jz.zhou@hust.edu.c [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Youlin; Wang Ying; Zhang Yongchuan [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-04-15

    A new multi-objective optimization method based on differential evolution with adaptive Cauchy mutation (MODE-ACM) is presented to solve short-term multi-objective optimal hydro-thermal scheduling (MOOHS) problem. Besides fuel cost, the pollutant gas emission is also optimized as an objective. The water transport delay between connected reservoirs and the effect of valve-point loading of thermal units are also taken into account in the presented problem formulation. The proposed algorithm adopts an elitist archive to retain non-dominated solutions obtained during the evolutionary process. It modifies the DE's operators to make it suit for multi-objective optimization (MOO) problems and improve its performance. Furthermore, to avoid premature convergence, an adaptive Cauchy mutation is proposed to preserve the diversity of population. An effective constraints handling method is utilized to handle the complex equality and inequality constraints. The effectiveness of the proposed algorithm is tested on a hydro-thermal system consisting of four cascaded hydro plants and three thermal units. The results obtained by MODE-ACM are compared with several previous studies. It is found that the results obtained by MODE-ACM are superior in terms of fuel cost as well as emission output, consuming a shorter time. Thus it can be a viable alternative to generate optimal trade-offs for short-term MOOHS problem.

  13. Non-convex multi-objective optimization

    CERN Document Server

    Pardalos, Panos M; Žilinskas, Julius

    2017-01-01

    Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in...

  14. Modelling formation of disinfection by-products in water distribution: Optimisation using a multi-objective evolutionary algorithm

    KAUST Repository

    Radhakrishnan, Mohanasundar; Pathirana, Assela; Ghebremichael, Kebreab A.; Amy, Gary L.

    2012-01-01

    Concerns have been raised regarding disinfection by-products (DBPs) formed as a result of the reaction of halogen-based disinfectants with DBP precursors. In order to appreciate the chemical and biological tradeoffs, it is imperative to understand the formation trends of DBPs and their spread in the distribution network. However, the water at a point in a complex distribution system is a mixture from various sources, whose proportions are complex to estimate and requires advanced hydraulic analysis. To understand the risks of DBPs and to develop mitigation strategies, it is important to understand the distribution of DBPs in a water network, which requires modelling. The goal of this research was to integrate a steady-state water network model with a particle backtracking algorithm and chlorination as well as DBPs models in order to assess the tradeoffs between biological and chemical risks in the distribution network. A multi-objective optimisation algorithm was used to identify the optimal proportion of water from various sources, dosages of alum, and dosages of chlorine in the treatment plant and in booster locations to control the formation of chlorination DBPs and to achieve a balance between microbial and chemical risks. © IWA Publishing 2012.

  15. Modelling formation of disinfection by-products in water distribution: Optimisation using a multi-objective evolutionary algorithm

    KAUST Repository

    Radhakrishnan, Mohanasundar

    2012-05-01

    Concerns have been raised regarding disinfection by-products (DBPs) formed as a result of the reaction of halogen-based disinfectants with DBP precursors. In order to appreciate the chemical and biological tradeoffs, it is imperative to understand the formation trends of DBPs and their spread in the distribution network. However, the water at a point in a complex distribution system is a mixture from various sources, whose proportions are complex to estimate and requires advanced hydraulic analysis. To understand the risks of DBPs and to develop mitigation strategies, it is important to understand the distribution of DBPs in a water network, which requires modelling. The goal of this research was to integrate a steady-state water network model with a particle backtracking algorithm and chlorination as well as DBPs models in order to assess the tradeoffs between biological and chemical risks in the distribution network. A multi-objective optimisation algorithm was used to identify the optimal proportion of water from various sources, dosages of alum, and dosages of chlorine in the treatment plant and in booster locations to control the formation of chlorination DBPs and to achieve a balance between microbial and chemical risks. © IWA Publishing 2012.

  16. A hybrid multi-objective cultural algorithm for short-term environmental/economic hydrothermal scheduling

    International Nuclear Information System (INIS)

    Lu Youlin; Zhou Jianzhong; Qin Hui; Wang Ying; Zhang Yongchuan

    2011-01-01

    Research highlights: → Multi-objective optimization model of short-term environmental/economic hydrothermal scheduling. → A hybrid multi-objective cultural algorithm (HMOCA) is presented. → New heuristic constraint handling methods are proposed. → Better quality solutions by reducing fuel cost and emission effects simultaneously are obtained. -- Abstract: The short-term environmental/economic hydrothermal scheduling (SEEHS) with the consideration of multiple objectives is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. In this paper, a multi-objective optimization model of SEEHS is proposed to consider the minimal of fuel cost and emission effects synthetically, and the transmission loss, the water transport delays between connected reservoirs as well as the valve-point effects of thermal plants are taken into consideration to formulate the problem precisely. Meanwhile, a hybrid multi-objective cultural algorithm (HMOCA) is presented to deal with SEEHS problem by optimizing both two objectives simultaneously. The proposed method integrated differential evolution (DE) algorithm into the framework of cultural algorithm model to implement the evolution of population space, and two knowledge structures in belief space are redefined according to the characteristics of DE and SEEHS problem to avoid premature convergence effectively. Moreover, in order to deal with the complicated constraints effectively, new heuristic constraint handling methods without any penalty factor settings are proposed in this paper. The feasibility and effectiveness of the proposed HMOCA method are demonstrated by two case studies of a hydrothermal power system. The simulation results reveal that, compared with other methods established recently, HMOCA can get better quality solutions by reducing fuel cost and emission effects simultaneously.

  17. A multi-objective approach to evolving platooning strategies in intelligent transportation systems

    NARCIS (Netherlands)

    Illigen, W. van; Haasdijk, E.; Kester, L.J.H.M.

    2013-01-01

    The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves highlevel

  18. A Multi-Objective Approach to Evolving Platooning Strategies in Intelligent Transportation Systems

    NARCIS (Netherlands)

    van Willigen, W; Haasdijk, E; Kester, Leon

    2013-01-01

    The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves high-level

  19. AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    P. Fischer

    2018-04-01

    Full Text Available This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

  20. MONSS: A multi-objective nonlinear simplex search approach

    Science.gov (United States)

    Zapotecas-Martínez, Saúl; Coello Coello, Carlos A.

    2016-01-01

    This article presents a novel methodology for dealing with continuous box-constrained multi-objective optimization problems (MOPs). The proposed algorithm adopts a nonlinear simplex search scheme in order to obtain multiple elements of the Pareto optimal set. The search is directed by a well-distributed set of weight vectors, each of which defines a scalarization problem that is solved by deforming a simplex according to the movements described by Nelder and Mead's method. Considering an MOP with n decision variables, the simplex is constructed using n+1 solutions which minimize different scalarization problems defined by n+1 neighbor weight vectors. All solutions found in the search are used to update a set of solutions considered to be the minima for each separate problem. In this way, the proposed algorithm collectively obtains multiple trade-offs among the different conflicting objectives, while maintaining a proper representation of the Pareto optimal front. In this article, it is shown that a well-designed strategy using just mathematical programming techniques can be competitive with respect to the state-of-the-art multi-objective evolutionary algorithms against which it was compared.

  1. A Novel Population-based Multi-Objective CMA-ES and the Impact of Different Constraint Handling Techniques

    NARCIS (Netherlands)

    S.F. Rodrigues; P. Bauer (Pavol); P.A.N. Bosman (Peter)

    2014-01-01

    htmlabstractThe Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a well-known, state-of-the-art optimization algorithm for single-objective real-valued problems, especially in black-box settings. Although several extensions of CMA-ES to multi-objective (MO) optimization exist, no

  2. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Maryam Mousavi

    Full Text Available Flexible manufacturing system (FMS enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs. An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA, particle swarm optimization (PSO, and hybrid GA-PSO to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  3. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Science.gov (United States)

    Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

    2017-01-01

    Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  4. Safety management in NPPs using an evolutionary algorithm technique

    International Nuclear Information System (INIS)

    Mishra, Alok; Patwardhan, Anand; Verma, A.K.

    2007-01-01

    The general goal of safety management in Nuclear Power Plants (NPPs) is to make requirements and activities more risk effective and less costly. The technical specification and maintenance (TS and M) activities in a plant are associated with controlling risk or with satisfying requirements, and are candidates to be evaluated for their resource effectiveness in risk-informed applications. Accordingly, the risk-based analysis of technical specification (RBTS) is being considered in evaluating current TS. The multi-objective optimization of the TS and M requirements of a NPP based on risk and cost, gives the pareto-optimal solutions, from which the utility can pick its decision variables suiting its interest. In this paper, a multi-objective evolutionary algorithm technique has been used to make a trade-off between risk and cost both at the system level and at the plant level for loss of coolant accident (LOCA) and main steam line break (MSLB) as initiating events

  5. Multi-objective optimization in the presence of practical constraints using non-dominated sorting hybrid cuckoo search algorithm

    Directory of Open Access Journals (Sweden)

    M. Balasubbareddy

    2015-12-01

    Full Text Available A novel optimization algorithm is proposed to solve single and multi-objective optimization problems with generation fuel cost, emission, and total power losses as objectives. The proposed method is a hybridization of the conventional cuckoo search algorithm and arithmetic crossover operations. Thus, the non-linear, non-convex objective function can be solved under practical constraints. The effectiveness of the proposed algorithm is analyzed for various cases to illustrate the effect of practical constraints on the objectives' optimization. Two and three objective multi-objective optimization problems are formulated and solved using the proposed non-dominated sorting-based hybrid cuckoo search algorithm. The effectiveness of the proposed method in confining the Pareto front solutions in the solution region is analyzed. The results for single and multi-objective optimization problems are physically interpreted on standard test functions as well as the IEEE-30 bus test system with supporting numerical and graphical results and also validated against existing methods.

  6. Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm

    International Nuclear Information System (INIS)

    Duan, Chen; Wang, Xinggang; Shu, Shuiming; Jing, Changwei; Chang, Huawei

    2014-01-01

    Highlights: • An improved thermodynamic model taking into account irreversibility parameter was developed. • A multi-objective optimization method for designing Stirling engine was investigated. • Multi-objective particle swarm optimization algorithm was adopted in the area of Stirling engine for the first time. - Abstract: In the recent years, the interest in Stirling engine has remarkably increased due to its ability to use any heat source from outside including solar energy, fossil fuels and biomass. A large number of studies have been done on Stirling cycle analysis. In the present study, a mathematical model based on thermodynamic analysis of Stirling engine considering regenerative losses and internal irreversibilities has been developed. Power output, thermal efficiency and the cycle irreversibility parameter of Stirling engine are optimized simultaneously using Particle Swarm Optimization (PSO) algorithm, which is more effective than traditional genetic algorithms. In this optimization problem, some important parameters of Stirling engine are considered as decision variables, such as temperatures of the working fluid both in the high temperature isothermal process and in the low temperature isothermal process, dead volume ratios of each heat exchanger, volumes of each working spaces, effectiveness of the regenerator, and the system charge pressure. The Pareto optimal frontier is obtained and the final design solution has been selected by Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Results show that the proposed multi-objective optimization approach can significantly outperform traditional single objective approaches

  7. Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

    Institute of Scientific and Technical Information of China (English)

    Lili Tao; Bin Xu; Zhihua Hu; Weimin Zhong

    2017-01-01

    The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta-neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob-lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

  8. Multi-objective PID Optimization for Speed Control of an Isolated Steam Turbine using Gentic Algorithm

    OpenAIRE

    Sanjay Kr. Singh; D. Boolchandani; S.G. Modani; Nitish Katal

    2014-01-01

    This study focuses on multi-objective optimization of the PID controllers for optimal speed control for an isolated steam turbine. In complex operations, optimal tuning plays an imperative role in maintaining the product quality and process safety. This study focuses on the comparison of the optimal PID tuning using Multi-objective Genetic Algorithm (NSGA-II) against normal genetic algorithm and Ziegler Nichols methods for the speed control of an isolated steam turbine. Isolated steam turbine...

  9. Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

    Science.gov (United States)

    Feng, Yen-Yi; Wu, I-Chin; Chen, Tzu-Li

    2017-03-01

    The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

  10. An evolutionary algorithm technique for intelligence, surveillance, and reconnaissance plan optimization

    Science.gov (United States)

    Langton, John T.; Caroli, Joseph A.; Rosenberg, Brad

    2008-04-01

    To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology

  11. A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

    NARCIS (Netherlands)

    Hamdy, M.; Nguyen, A.T. (Anh Tuan); Hensen, J.L.M.

    2016-01-01

    Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design

  12. Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

    International Nuclear Information System (INIS)

    Sayyaadi, Hoseyn

    2009-01-01

    Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied

  13. Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

    Science.gov (United States)

    Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

    2016-01-01

    Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

  14. Design of a Fractional Order Frequency PID Controller for an Islanded Microgrid: A Multi-Objective Extremal Optimization Method

    Directory of Open Access Journals (Sweden)

    Huan Wang

    2017-10-01

    Full Text Available Fractional order proportional-integral-derivative(FOPID controllers have attracted increasing attentions recently due to their better control performance than the traditional integer-order proportional-integral-derivative (PID controllers. However, there are only few studies concerning the fractional order control of microgrids based on evolutionary algorithms. From the perspective of multi-objective optimization, this paper presents an effective FOPID based frequency controller design method called MOEO-FOPID for an islanded microgrid by using a Multi-objective extremal optimization (MOEO algorithm to minimize frequency deviation and controller output signal simultaneously in order to improve finally the efficient operation of distributed generations and energy storage devices. Its superiority to nondominated sorting genetic algorithm-II (NSGA-II based FOPID/PID controllers and other recently reported single-objective evolutionary algorithms such as Kriging-based surrogate modeling and real-coded population extremal optimization-based FOPID controllers is demonstrated by the simulation studies on a typical islanded microgrid in terms of the control performance including frequency deviation, deficit grid power, controller output signal and robustness.

  15. An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning

    Directory of Open Access Journals (Sweden)

    Nizar Hadi Abbas

    2016-07-01

    Full Text Available This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper pa‌th from the start to the destination position with minimum distance and time.

  16. 8th International Conference on Multi-Objective and Goal Programming

    CERN Document Server

    Tamiz, Mehrdad; Ries, Jana

    2010-01-01

    This volume shows the state-of-the-art in both theoretical development and application of multiple objective and goal programming. Applications from the fields of supply chain management, financial portfolio selection, financial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, water resource management, and the interface with data envelopment analysis give a good reflection of current usage. A pleasing variety of techniques are used including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. The book is based on the papers of the 8th International Conference on Multi-Objective and Goal Programming (MOPGP08) which was held in Portsmouth, UK, in September 2008.

  17. Optimization of Combined Thermal and Electrical Behavior of Power Converters Using Multi-Objective Genetic Algorithms

    NARCIS (Netherlands)

    Malyna, D.V.; Duarte, J.L.; Hendrix, M.A.M.; Horck, van F.B.M.

    2007-01-01

    A practical example of power electronic converter synthesis is presented, where a multi-objective genetic algorithm, namely non-dominated sorting genetic algorithm (NSGA-II) is used. The optimization algorithm takes an experimentally-derived thermal model for the converter into account. Experimental

  18. Diversity-Guided Evolutionary Algorithms

    DEFF Research Database (Denmark)

    Ursem, Rasmus Kjær

    2002-01-01

    Population diversity is undoubtably a key issue in the performance of evolutionary algorithms. A common hypothesis is that high diversity is important to avoid premature convergence and to escape local optima. Various diversity measures have been used to analyze algorithms, but so far few...... algorithms have used a measure to guide the search. The diversity-guided evolutionary algorithm (DGEA) uses the wellknown distance-to-average-point measure to alternate between phases of exploration (mutation) and phases of exploitation (recombination and selection). The DGEA showed remarkable results...

  19. A performance-oriented power transformer design methodology using multi-objective evolutionary optimization.

    Science.gov (United States)

    Adly, Amr A; Abd-El-Hafiz, Salwa K

    2015-05-01

    Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

  20. Multi-objective genetic algorithm for solving N-version program design problem

    Energy Technology Data Exchange (ETDEWEB)

    Yamachi, Hidemi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan) and Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamachi@nit.ac.jp; Tsujimura, Yasuhiro [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: tujimr@nit.ac.jp; Kambayashi, Yasushi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: yasushi@nit.ac.jp; Yamamoto, Hisashi [Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamamoto@cc.tmit.ac.jp

    2006-09-15

    N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.

  1. Multi-objective genetic algorithm for solving N-version program design problem

    International Nuclear Information System (INIS)

    Yamachi, Hidemi; Tsujimura, Yasuhiro; Kambayashi, Yasushi; Yamamoto, Hisashi

    2006-01-01

    N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost

  2. An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Xuanhu He

    2015-03-01

    Full Text Available Optimal power flow (OPF objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

  3. Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

    Directory of Open Access Journals (Sweden)

    Man Ding

    2017-03-01

    Full Text Available This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP, which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII and multi-objective ABC (MOABC, are presented to illustrate the effectiveness and robustness of the proposed method.

  4. Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

    Science.gov (United States)

    Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

    2018-03-01

    In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

  5. A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

    Science.gov (United States)

    Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

    2015-01-01

    Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

  6. NSGA-II algorithm for multi-objective generation expansion planning problem

    Energy Technology Data Exchange (ETDEWEB)

    Murugan, P.; Kannan, S. [Electronics and Communication Engineering Department, Arulmigu Kalasalingam College of Engineering, Krishnankoil 626190, Tamilnadu (India); Baskar, S. [Electrical Engineering Department, Thiagarajar College of Engineering, Madurai 625015, Tamilnadu (India)

    2009-04-15

    This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II), to multi-objective generation expansion planning (GEP) problem. The GEP problem is considered as a two-objective problem. The first objective is the minimization of investment cost and the second objective is the minimization of outage cost (or maximization of reliability). To improve the performance of NSGA-II, two modifications are proposed. One modification is incorporation of Virtual Mapping Procedure (VMP), and the other is introduction of controlled elitism in NSGA-II. A synthetic test system having 5 types of candidate units is considered here for GEP for a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail. (author)

  7. Design and selection of load control strategies using a multiple objective model and evolutionary algorithms

    International Nuclear Information System (INIS)

    Gomes, Alvaro; Antunes, Carlos Henggeler; Martins, Antonio Gomes

    2005-01-01

    This paper aims at presenting a multiple objective model to evaluate the attractiveness of the use of demand resources (through load management control actions) by different stakeholders and in diverse structure scenarios in electricity systems. For the sake of model flexibility, the multiple (and conflicting) objective functions of technical, economical and quality of service nature are able to capture distinct market scenarios and operating entities that may be interested in promoting load management activities. The computation of compromise solutions is made by resorting to evolutionary algorithms, which are well suited to tackle multiobjective problems of combinatorial nature herein involving the identification and selection of control actions to be applied to groups of loads. (Author)

  8. Investigating the Multi-memetic Mind Evolutionary Computation Algorithm Efficiency

    Directory of Open Access Journals (Sweden)

    M. K. Sakharov

    2017-01-01

    Full Text Available In solving practically significant problems of global optimization, the objective function is often of high dimensionality and computational complexity and of nontrivial landscape as well. Studies show that often one optimization method is not enough for solving such problems efficiently - hybridization of several optimization methods is necessary.One of the most promising contemporary trends in this field are memetic algorithms (MA, which can be viewed as a combination of the population-based search for a global optimum and the procedures for a local refinement of solutions (memes, provided by a synergy. Since there are relatively few theoretical studies concerning the MA configuration, which is advisable for use to solve the black-box optimization problems, many researchers tend just to adaptive algorithms, which for search select the most efficient methods of local optimization for the certain domains of the search space.The article proposes a multi-memetic modification of a simple SMEC algorithm, using random hyper-heuristics. Presents the software algorithm and memes used (Nelder-Mead method, method of random hyper-sphere surface search, Hooke-Jeeves method. Conducts a comparative study of the efficiency of the proposed algorithm depending on the set and the number of memes. The study has been carried out using Rastrigin, Rosenbrock, and Zakharov multidimensional test functions. Computational experiments have been carried out for all possible combinations of memes and for each meme individually.According to results of study, conducted by the multi-start method, the combinations of memes, comprising the Hooke-Jeeves method, were successful. These results prove a rapid convergence of the method to a local optimum in comparison with other memes, since all methods perform the fixed number of iterations at the most.The analysis of the average number of iterations shows that using the most efficient sets of memes allows us to find the optimal

  9. A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Wanxing Sheng

    2013-01-01

    Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

  10. Aida-CMK multi-algorithm optimization kernel applied to analog IC sizing

    CERN Document Server

    Lourenço, Ricardo; Horta, Nuno

    2015-01-01

    This work addresses the research and development of an innovative optimization kernel applied to analog integrated circuit (IC) design. Particularly, this works describes the modifications inside the AIDA Framework, an electronic design automation framework fully developed by at the Integrated Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, by enhancing AIDA-C, which is the circuit optimizer component of AIDA, with a new multi-objective multi-constraint optimization module that constructs a base for multiple algorithm implementations. The proposed solution implements three approaches to multi-objective multi-constraint optimization, namely, an evolutionary approach with NSGAII, a swarm intelligence approach with MOPSO and stochastic hill climbing approach with MOSA. Moreover, the implemented structure allows the easy hybridization between kernels transforming the previous simple NSGAII optimization module into a more evolved and versatile module supporting multiple s...

  11. Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

    International Nuclear Information System (INIS)

    Zio, E.; Baraldi, P.; Pedroni, N.

    2009-01-01

    Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants. As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision-maker (DM) must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the DM with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto-optimal set. In this paper, ways are explored to use this preference information for driving a multi-objective genetic algorithm towards the preferential region of the Pareto-optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights that express the DM' s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the DM. The effectiveness of the proposed approaches is first compared on a case study of literature. Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled

  12. A Hybrid Chaotic Quantum Evolutionary Algorithm

    DEFF Research Database (Denmark)

    Cai, Y.; Zhang, M.; Cai, H.

    2010-01-01

    A hybrid chaotic quantum evolutionary algorithm is proposed to reduce amount of computation, speed up convergence and restrain premature phenomena of quantum evolutionary algorithm. The proposed algorithm adopts the chaotic initialization method to generate initial population which will form a pe...... tests. The presented algorithm is applied to urban traffic signal timing optimization and the effect is satisfied....

  13. Species co-evolutionary algorithm: a novel evolutionary algorithm based on the ecology and environments for optimization

    DEFF Research Database (Denmark)

    Li, Wuzhao; Wang, Lei; Cai, Xingjuan

    2015-01-01

    and affect each other in many ways. The relationships include competition, predation, parasitism, mutualism and pythogenesis. In this paper, we consider the five relationships between solutions to propose a co-evolutionary algorithm termed species co-evolutionary algorithm (SCEA). In SCEA, five operators...

  14. A probabilistic multi objective CLSC model with Genetic algorithm-ε_Constraint approach

    Directory of Open Access Journals (Sweden)

    Alireza TaheriMoghadam

    2014-05-01

    Full Text Available In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization and for finding the pareto optimal line, Epsilon-constraint method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

  15. Industrial Applications of Evolutionary Algorithms

    CERN Document Server

    Sanchez, Ernesto; Tonda, Alberto

    2012-01-01

    This book is intended as a reference both for experienced users of evolutionary algorithms and for researchers that are beginning to approach these fascinating optimization techniques. Experienced users will find interesting details of real-world problems, and advice on solving issues related to fitness computation, modeling and setting appropriate parameters to reach optimal solutions. Beginners will find a thorough introduction to evolutionary computation, and a complete presentation of all evolutionary algorithms exploited to solve different problems. The book could fill the gap between the

  16. Evolutionary Bi-objective Optimization for Bulldozer and Its Blade in Soil Cutting

    Science.gov (United States)

    Sharma, Deepak; Barakat, Nada

    2018-02-01

    An evolutionary optimization approach is adopted in this paper for simultaneously achieving the economic and productive soil cutting. The economic aspect is defined by minimizing the power requirement from the bulldozer, and the soil cutting is made productive by minimizing the time of soil cutting. For determining the power requirement, two force models are adopted from the literature to quantify the cutting force on the blade. Three domain-specific constraints are also proposed, which are limiting the power from the bulldozer, limiting the maximum force on the bulldozer blade and achieving the desired production rate. The bi-objective optimization problem is solved using five benchmark multi-objective evolutionary algorithms and one classical optimization technique using the ɛ-constraint method. The Pareto-optimal solutions are obtained with the knee-region. Further, the post-optimal analysis is performed on the obtained solutions to decipher relationships among the objectives and decision variables. Such relationships are later used for making guidelines for selecting the optimal set of input parameters. The obtained results are then compared with the experiment results from the literature that show a close agreement among them.

  17. The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

    Directory of Open Access Journals (Sweden)

    Dazhi Jiang

    2015-01-01

    Full Text Available At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.

  18. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

    Science.gov (United States)

    Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

    2017-05-01

    In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

  19. Multi-Objective Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using the Non-Dominated Sorting Genetic Algorithm II and Finite Element Method

    Directory of Open Access Journals (Sweden)

    Jie Zhu

    2014-02-01

    Full Text Available A multi-objective optimization method for the structural design of horizontal-axis wind turbine (HAWT blades is presented. The main goal is to minimize the weight and cost of the blade which uses glass fiber reinforced plastic (GFRP coupled with carbon fiber reinforced plastic (CFRP materials. The number and the location of layers in the spar cap, the width of the spar cap and the position of the shear webs are employed as the design variables, while the strain limit, blade/tower clearance limit and vibration limit are taken into account as the constraint conditions. The optimization of the design of a commercial 1.5 MW HAWT blade is carried out by combining FEM analysis and a multi-objective evolutionary algorithm under ultimate (extreme flap-wise load and edge-wise load conditions. The best solutions are described and the comparison of the obtained results with the original design is performed to prove the efficiency and applicability of the method.

  20. CCS Site Optimization by Applying a Multi-objective Evolutionary Algorithm to Semi-Analytical Leakage Models

    Science.gov (United States)

    Cody, B. M.; Gonzalez-Nicolas, A.; Bau, D. A.

    2011-12-01

    Carbon capture and storage (CCS) has been proposed as a method of reducing global carbon dioxide (CO2) emissions. Although CCS has the potential to greatly retard greenhouse gas loading to the atmosphere while cleaner, more sustainable energy solutions are developed, there is a possibility that sequestered CO2 may leak and intrude into and adversely affect groundwater resources. It has been reported [1] that, while CO2 intrusion typically does not directly threaten underground drinking water resources, it may cause secondary effects, such as the mobilization of hazardous inorganic constituents present in aquifer minerals and changes in pH values. These risks must be fully understood and minimized before CCS project implementation. Combined management of project resources and leakage risk is crucial for the implementation of CCS. In this work, we present a method of: (a) minimizing the total CCS cost, the summation of major project costs with the cost associated with CO2 leakage; and (b) maximizing the mass of injected CO2, for a given proposed sequestration site. Optimization decision variables include the number of CO2 injection wells, injection rates, and injection well locations. The capital and operational costs of injection wells are directly related to injection well depth, location, injection flow rate, and injection duration. The cost of leakage is directly related to the mass of CO2 leaked through weak areas, such as abandoned oil wells, in the cap rock layers overlying the injected formation. Additional constraints on fluid overpressure caused by CO2 injection are imposed to maintain predefined effective stress levels that prevent cap rock fracturing. Here, both mass leakage and fluid overpressure are estimated using two semi-analytical models based upon work by [2,3]. A multi-objective evolutionary algorithm coupled with these semi-analytical leakage flow models is used to determine Pareto-optimal trade-off sets giving minimum total cost vs. maximum mass

  1. Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation.

    Science.gov (United States)

    Liu, Xiaofeng; Bai, Fang; Ouyang, Sisheng; Wang, Xicheng; Li, Honglin; Jiang, Hualiang

    2009-03-31

    Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of

  2. Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation

    Directory of Open Access Journals (Sweden)

    Li Honglin

    2009-03-01

    Full Text Available Abstract Background Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. Results The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105–112. Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 Å to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 ± 0.18 seconds per molecule renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. Conclusion On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms

  3. Multi-objective random search algorithm for simultaneously optimizing wind farm layout and number of turbines

    DEFF Research Database (Denmark)

    Feng, Ju; Shen, Wen Zhong; Xu, Chang

    2016-01-01

    A new algorithm for multi-objective wind farm layout optimization is presented. It formulates the wind turbine locations as continuous variables and is capable of optimizing the number of turbines and their locations in the wind farm simultaneously. Two objectives are considered. One is to maximi...

  4. Aggregate meta-models for evolutionary multiobjective and many-objective optimization

    Czech Academy of Sciences Publication Activity Database

    Pilát, Martin; Neruda, Roman

    Roč. 116, 20 September (2013), s. 392-402 ISSN 0925-2312 R&D Projects: GA ČR GAP202/11/1368 Institutional support: RVO:67985807 Keywords : evolutionary algorithms * multiobjective optimization * many-objective optimization * surrogate models * meta-models * memetic algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 2.005, year: 2013

  5. Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies

    International Nuclear Information System (INIS)

    Safari, Jalal

    2012-01-01

    This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed.

  6. Benchmarks for dynamic multi-objective optimisation

    CSIR Research Space (South Africa)

    Helbig, M

    2013-06-01

    Full Text Available When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), benchmark functions should be used to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for dynamic multi...

  7. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

    Science.gov (United States)

    Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

    2017-05-01

    In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Optimum analysis of pavement maintenance using multi-objective genetic algorithms

    Directory of Open Access Journals (Sweden)

    Amr A. Elhadidy

    2015-04-01

    Full Text Available Road network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time. A multi-objective optimization problem for pavement maintenance and rehabilitation strategies on network level is discussed in this paper. A two-objective optimization model considers minimum action costs and maximum condition for used road network. In the proposed approach, Markov-chain models are used for predicting the performance of road pavement and to calculate the expected decline at different periods of time. A genetic-algorithm-based procedure is developed for solving the multi-objective optimization problem. The model searched for the optimum maintenance actions at adequate time to be implemented on an appropriate pavement. Based on the computing results, the Pareto optimal solutions of the two-objective optimization functions are obtained. From the optimal solutions represented by cost and condition, a decision maker can easily obtain the information of the maintenance and rehabilitation planning with minimum action costs and maximum condition. The developed model has been implemented on a network of roads and showed its ability to derive the optimal solution.

  9. Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation

    International Nuclear Information System (INIS)

    Elegbede, Charles; Adjallah, Kondo

    2003-01-01

    This paper describes a methodology based on genetic algorithms (GA) and experiments plan to optimize the availability and the cost of reparable parallel-series systems. It is a NP-hard problem of multi-objective combinatorial optimization, modeled with continuous and discrete variables. By using the weighting technique, the problem is transformed into a single-objective optimization problem whose constraints are then relaxed by the exterior penalty technique. We then propose a search of solution through GA, whose parameters are adjusted using experiments plan technique. A numerical example is used to assess the method

  10. Evolutionary algorithms for mobile ad hoc networks

    CERN Document Server

    Dorronsoro, Bernabé; Danoy, Grégoire; Pigné, Yoann; Bouvry, Pascal

    2014-01-01

    Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking. Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms topology management, and mobility models to address challenges in the ...

  11. Evaluating the Efficiency of a Multi-core Aware Multi-objective Optimization Tool for Calibrating the SWAT Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, X. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Izaurralde, R. C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zong, Z. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zhao, K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Thomson, A. M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2012-08-20

    The efficiency of calibrating physically-based complex hydrologic models is a major concern in the application of those models to understand and manage natural and human activities that affect watershed systems. In this study, we developed a multi-core aware multi-objective evolutionary optimization algorithm (MAMEOA) to improve the efficiency of calibrating a worldwide used watershed model (Soil and Water Assessment Tool (SWAT)). The test results show that MAMEOA can save about 1-9%, 26-51%, and 39-56% time consumed by calibrating SWAT as compared with sequential method by using dual-core, quad-core, and eight-core machines, respectively. Potential and limitations of MAMEOA for calibrating SWAT are discussed. MAMEOA is open source software.

  12. Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

    Energy Technology Data Exchange (ETDEWEB)

    Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.

    2017-07-01

    The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world

  13. Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

    Directory of Open Access Journals (Sweden)

    Muhammad Hashim

    2017-05-01

    Full Text Available Purpose:  The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM. Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM with fuzzy coefficients constructed in present research should be helpful for

  14. Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

    International Nuclear Information System (INIS)

    Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.

    2017-01-01

    The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world

  15. Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Li Ran

    2017-01-01

    Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.

  16. Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

    Science.gov (United States)

    Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

    2017-07-01

    Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Bi-objective branch-and-cut algorithms

    DEFF Research Database (Denmark)

    Gadegaard, Sune Lauth; Ehrgott, Matthias; Nielsen, Lars Relund

    Most real-world optimization problems are of a multi-objective nature, involving objectives which are conflicting and incomparable. Solving a multi-objective optimization problem requires a method which can generate the set of rational compromises between the objectives. In this paper, we propose...... are strengthened by cutting planes. In addition, we suggest an extension of the branching strategy "Pareto branching''. Extensive computational results obtained for the bi-objective single source capacitated facility location problem prove the effectiveness of the algorithms....... and compares it to an upper bound set. The implicit bound set based algorithm, on the other hand, fathoms branching nodes by generating a single point on the lower bound set for each local nadir point. We outline several approaches for fathoming branching nodes and we propose an updating scheme for the lower...

  18. Introduction to Evolutionary Algorithms

    CERN Document Server

    Yu, Xinjie

    2010-01-01

    Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm opti

  19. An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Kulkarni, Nandkumar P.; Prasad, Neeli R.; Prasad, Ramjee

    deliberation. To tackle these two problems, Mobile Wireless Sensor Networks (MWSNs) is a better choice. In MWSN, Sensor nodes move freely to a target area without the need for any special infrastructure. Due to mobility, the routing process in MWSN has become more complicated as connections in the network can...... such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The authors study the influence of energy heterogeneity and mobility of sensor nodes on the performance of EMRP. The Performance of EMRP compared with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing...

  20. MULTI-OBJECTIVE OPTIMISATION OF LASER CUTTING USING CUCKOO SEARCH ALGORITHM

    Directory of Open Access Journals (Sweden)

    M. MADIĆ

    2015-03-01

    Full Text Available Determining of optimal laser cutting conditions for improving cut quality characteristics is of great importance in process planning. This paper presents multi-objective optimisation of the CO2 laser cutting process considering three cut quality characteristics such as surface roughness, heat affected zone (HAZ and kerf width. It combines an experimental design by using Taguchi’s method, modelling the relationships between the laser cutting factors (laser power, cutting speed, assist gas pressure and focus position and cut quality characteristics by artificial neural networks (ANNs, formulation of the multiobjective optimisation problem using weighting sum method, and solving it by the novel meta-heuristic cuckoo search algorithm (CSA. The objective is to obtain optimal cutting conditions dependent on the importance order of the cut quality characteristics for each of four different case studies presented in this paper. The case studies considered in this study are: minimisation of cut quality characteristics with equal priority, minimisation of cut quality characteristics with priority given to surface roughness, minimisation of cut quality characteristics with priority given to HAZ, and minimisation of cut quality characteristics with priority given to kerf width. The results indicate that the applied CSA for solving the multi-objective optimisation problem is effective, and that the proposed approach can be used for selecting the optimal laser cutting factors for specific production requirements.

  1. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    Energy Technology Data Exchange (ETDEWEB)

    Bellary, Sayed Ahmed Imran; Samad, Abdus [Indian Institute of Technology Madras, Chennai (India); Husain, Afzal [Sultan Qaboos University, Al-Khoudh (Oman)

    2014-12-15

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  2. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    International Nuclear Information System (INIS)

    Bellary, Sayed Ahmed Imran; Samad, Abdus; Husain, Afzal

    2014-01-01

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  3. Multi-objective genetic algorithm based innovative wind farm layout optimization method

    International Nuclear Information System (INIS)

    Chen, Ying; Li, Hua; He, Bang; Wang, Pengcheng; Jin, Kai

    2015-01-01

    Highlights: • Innovative optimization procedures for both regular and irregular shape wind farm. • Using real wind condition and commercial wind turbine parameters. • Using multiple-objective genetic algorithm optimization method. • Optimize the selection of different wind turbine types and their hub heights. - Abstract: Layout optimization has become one of the critical approaches to increase power output and decrease total cost of a wind farm. Previous researches have applied intelligent algorithms to optimizing the wind farm layout. However, those wind conditions used in most of previous research are simplified and not accurate enough to match the real world wind conditions. In this paper, the authors propose an innovative optimization method based on multi-objective genetic algorithm, and test it with real wind condition and commercial wind turbine parameters. Four case studies are conducted to investigate the number of wind turbines needed in the given wind farm. Different cost models are also considered in the case studies. The results clearly demonstrate that the new method is able to optimize the layout of a given wind farm with real commercial data and wind conditions in both regular and irregular shapes, and achieve a better result by selecting different type and hub height wind turbines.

  4. Optimum oil production planning using infeasibility driven evolutionary algorithm.

    Science.gov (United States)

    Singh, Hemant Kumar; Ray, Tapabrata; Sarker, Ruhul

    2013-01-01

    In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.

  5. A FAST AND ELITIST BI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR SCHEDULING INDEPENDENT TASKS ON HETEROGENEOUS SYSTEMS

    Directory of Open Access Journals (Sweden)

    G.Subashini

    2010-07-01

    Full Text Available To meet the increasing computational demands, geographically distributed resources need to be logically coupled to make them work as a unified resource. In analyzing the performance of such distributed heterogeneous computing systems scheduling a set of tasks to the available set of resources for execution is highly important. Task scheduling being an NP-complete problem, use of metaheuristics is more appropriate in obtaining optimal solutions. Schedules thus obtained can be evaluated using several criteria that may conflict with one another which require multi objective problem formulation. This paper investigates the application of an elitist Nondominated Sorting Genetic Algorithm (NSGA-II, to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. The objectives considered in this paper include minimizing makespan and average flowtime simultaneously. The implementation of NSGA-II algorithm and Weighted-Sum Genetic Algorithm (WSGA has been tested on benchmark instances for distributed heterogeneous systems. As NSGA-II generates a set of Pareto optimal solutions, to verify the effectiveness of NSGA-II over WSGA a fuzzy based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set.

  6. A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation

    Directory of Open Access Journals (Sweden)

    Lina Yang

    2018-02-01

    Full Text Available Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by state-of-the-art algorithms is still not sufficient. To achieve better Pareto solutions, taking the grid-representative land-use allocation problem with two objectives as an example, an artificial bee colony optimization algorithm for multi-objective land-use allocation (ABC-MOLA is proposed. In this algorithm, the traditional ABC’s search direction guiding scheme and solution maintaining process are modified. In addition, a knowledge-informed neighborhood search strategy, which utilizes the auxiliary knowledge of natural geography and spatial structures to facilitate the neighborhood spatial search around each solution, is developed to further improve the Pareto front’s quality. A series of comparison experiments (a simulated experiment with small data volume and a real-world data experiment for a large area shows that all the Pareto fronts obtained by ABC-MOLA totally dominate the Pareto fronts by other algorithms, which demonstrates ABC-MOLA’s effectiveness in achieving Pareto fronts of high quality.

  7. Evolutionary constrained optimization

    CERN Document Server

    Deb, Kalyanmoy

    2015-01-01

    This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful...

  8. A multi-objective optimization for brush monofilament tufting process design

    Directory of Open Access Journals (Sweden)

    Ali Salmasnia

    2018-01-01

    Full Text Available This paper addresses the optimization of monofilament tufting process as the most important and the main stage of toothbrush production in sanitary industries. In order to minimize both process time and depreciation costs, and ultimately increase the production efficiency in such an industrial unit, we propose a metaheuristic based optimization approach to solve it. The Traveling Salesman Problem (TSP is used to formulate the proposed problem. Then by using multi-objective evolutionary algorithms, NSGA-II and MOPSO, we seek to obtain the best solution and objective functions described above. Extensive computational experiments on three different kinds of toothbrush handles are performed and the results demonstrate the applicability and appropriate performance of algorithms. The comparison metrics like spacing, number of Pareto solutions, time, mean distance from the ideal solution and diversity are used to evaluate the quality of solutions. Moreover a sensitivity analysis is done for investigation of the performance in various setting of parameters.

  9. Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

    Directory of Open Access Journals (Sweden)

    Jarosław Rudy

    2015-01-01

    Full Text Available In this paper the job shop scheduling problem (JSP with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO and genetic algorithm (GA for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

  10. Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-Ⅱ

    Institute of Scientific and Technical Information of China (English)

    Xi JIN; Jie ZHANG; Jin-liang GAO; Wen-yan WU

    2008-01-01

    Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Aigorithm-Ⅱ (NSGA-Ⅱ) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-Ⅱ into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.

  11. Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

    International Nuclear Information System (INIS)

    Pang, X.; Rybarcyk, L.J.

    2014-01-01

    Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster

  12. Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

    Energy Technology Data Exchange (ETDEWEB)

    Pang, X., E-mail: xpang@lanl.gov; Rybarcyk, L.J.

    2014-03-21

    Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster.

  13. A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

    International Nuclear Information System (INIS)

    Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

    2016-01-01

    Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

  14. Power magnetic devices a multi-objective design approach

    CERN Document Server

    Sudhoff, Scott D

    2014-01-01

    Presents a multi-objective design approach to the many power magnetic devices in use today Power Magnetic Devices: A Multi-Objective Design Approach addresses the design of power magnetic devices-including inductors, transformers, electromagnets, and rotating electric machinery-using a structured design approach based on formal single- and multi-objective optimization. The book opens with a discussion of evolutionary-computing-based optimization. Magnetic analysis techniques useful to the design of all the devices considered in the book are then set forth. This material is then used for ind

  15. Multi-objective optimization of generalized reliability design problems using feature models-A concept for early design stages

    International Nuclear Information System (INIS)

    Limbourg, Philipp; Kochs, Hans-Dieter

    2008-01-01

    Reliability optimization problems such as the redundancy allocation problem (RAP) have been of considerable interest in the past. However, due to the restrictions of the design space formulation, they may not be applicable in all practical design problems. A method with high modelling freedom for rapid design screening is desirable, especially in early design stages. This work presents a novel approach to reliability optimization. Feature modelling, a specification method originating from software engineering, is applied for the fast specification and enumeration of complex design spaces. It is shown how feature models can not only describe arbitrary RAPs but also much more complex design problems. The design screening is accomplished by a multi-objective evolutionary algorithm for probabilistic objectives. Comparing averages or medians may hide the true characteristics of this distributions. Therefore the algorithm uses solely the probability of a system dominating another to achieve the Pareto optimal set. We illustrate the approach by specifying a RAP and a more complex design space and screening them with the evolutionary algorithm

  16. Impact of fuel cell power plants on multi-objective optimal operation management of distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, T. [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran, Islamic Republic of); Zeinoddini-Meymand, H. [Islamic Azad University, Kerman Branch, Kerman (Iran, Islamic Republic of)

    2012-06-15

    This paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO-DE) to solve the multi-objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  17. Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

    Directory of Open Access Journals (Sweden)

    Jing Qiu

    2017-07-01

    Full Text Available This paper presents a multi-objective transmission expansion planning (TEP framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver’s six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

  18. A hybrid non-dominated sorting genetic algorithm and its application on multi-objective optimal design of nuclear power plant

    International Nuclear Information System (INIS)

    Chen, Lei; Yan, Changqi; Liao, Yi; Song, Feifei; Jia, Zhen

    2017-01-01

    Highlights: • The optimization ability of NSGA-II is improved. • The design targets can be obvious optimized through optimization methodology. • Multi-objective optimization is implanted into the design of nuclear power plant. - Abstract: The design of nuclear component can be optimized by seeking out the best combination of article operational and structural parameters. Through multi-objective optimization, the optimized scheme can not only meets the design requirements, but also satisfies the safety regulations. In this work, a hybrid non-dominated sorting genetic algorithm is proposed, and its performance is verified by comparing it with its prototype and immune memory clone constraint multi-objective algorithm through four test-functions; the designs of the steam generator and the primary loop of Qinshan I nuclear power plant are optimized by the proposed algorithm. The results show that the algorithm outperforms the other two through overall evaluation; the reactor inlet temperature is an important parameter which influences the distribution of the Pareto optimal front; through optimization, the weight of the steam generator can be reduced by 16.5%, and the primary flow-rate can be reduced by 17.0%, the weight of the primary loop can be reduced by 11.4%, and the volume can be reduced by 9.8%.

  19. Solving advanced multi-objective robust designs by means of multiple objective evolutionary algorithms (MOEA): A reliability application

    Energy Technology Data Exchange (ETDEWEB)

    Salazar A, Daniel E. [Division de Computacion Evolutiva (CEANI), Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Universidad de Las Palmas de Gran Canaria. Canary Islands (Spain)]. E-mail: danielsalazaraponte@gmail.com; Rocco S, Claudio M. [Universidad Central de Venezuela, Facultad de Ingenieria, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve

    2007-06-15

    This paper extends the approach proposed by the second author in [Rocco et al. Robust design using a hybrid-cellular-evolutionary and interval-arithmetic approach: a reliability application. In: Tarantola S, Saltelli A, editors. SAMO 2001: Methodological advances and useful applications of sensitivity analysis. Reliab Eng Syst Saf 2003;79(2):149-59 [special issue

  20. Investigating multi-objective fluence and beam orientation IMRT optimization

    Science.gov (United States)

    Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

    2017-07-01

    Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters

  1. Infrastructure system restoration planning using evolutionary algorithms

    Science.gov (United States)

    Corns, Steven; Long, Suzanna K.; Shoberg, Thomas G.

    2016-01-01

    This paper presents an evolutionary algorithm to address restoration issues for supply chain interdependent critical infrastructure. Rapid restoration of infrastructure after a large-scale disaster is necessary to sustaining a nation's economy and security, but such long-term restoration has not been investigated as thoroughly as initial rescue and recovery efforts. A model of the Greater Saint Louis Missouri area was created and a disaster scenario simulated. An evolutionary algorithm is used to determine the order in which the bridges should be repaired based on indirect costs. Solutions were evaluated based on the reduction of indirect costs and the restoration of transportation capacity. When compared to a greedy algorithm, the evolutionary algorithm solution reduced indirect costs by approximately 12.4% by restoring automotive travel routes for workers and re-establishing the flow of commodities across the three rivers in the Saint Louis area.

  2. Multi-objective optimization of an underwater compressed air energy storage system using genetic algorithm

    International Nuclear Information System (INIS)

    Cheung, Brian C.; Carriveau, Rupp; Ting, David S.K.

    2014-01-01

    This paper presents the findings from a multi-objective genetic algorithm optimization study on the design parameters of an underwater compressed air energy storage system (UWCAES). A 4 MWh UWCAES system was numerically simulated and its energy, exergy, and exergoeconomics were analysed. Optimal system configurations were determined that maximized the UWCAES system round-trip efficiency and operating profit, and minimized the cost rate of exergy destruction and capital expenditures. The optimal solutions obtained from the multi-objective optimization model formed a Pareto-optimal front, and a single preferred solution was selected using the pseudo-weight vector multi-criteria decision making approach. A sensitivity analysis was performed on interest rates to gauge its impact on preferred system designs. Results showed similar preferred system designs for all interest rates in the studied range. The round-trip efficiency and operating profit of the preferred system designs were approximately 68.5% and $53.5/cycle, respectively. The cost rate of the system increased with interest rates. - Highlights: • UWCAES system configurations were developed using multi-objective optimization. • System was optimized for energy efficiency, exergy, and exergoeconomics • Pareto-optimal solution surfaces were developed at different interest rates. • Similar preferred system configurations were found at all interest rates studied

  3. An Evolutionary Multi-objective Approach for Speed Tuning Optimization with Energy Saving in Railway Management

    OpenAIRE

    Chevrier , Rémy

    2010-01-01

    International audience; An approach for speed tuning in railway management is presented for optimizing both travel duration and energy saving. This approach is based on a state-of-the-art evolutionary algorithm with Pareto approach. This algorithm provides a set of diversified non-dominated solutions to the decision-maker. A case study on Gonesse connection (France) is also reported and analyzed.

  4. Expert-guided evolutionary algorithm for layout design of complex space stations

    Science.gov (United States)

    Qian, Zhiqin; Bi, Zhuming; Cao, Qun; Ju, Weiguo; Teng, Hongfei; Zheng, Yang; Zheng, Siyu

    2017-08-01

    The layout of a space station should be designed in such a way that different equipment and instruments are placed for the station as a whole to achieve the best overall performance. The station layout design is a typical nondeterministic polynomial problem. In particular, how to manage the design complexity to achieve an acceptable solution within a reasonable timeframe poses a great challenge. In this article, a new evolutionary algorithm has been proposed to meet such a challenge. It is called as the expert-guided evolutionary algorithm with a tree-like structure decomposition (EGEA-TSD). Two innovations in EGEA-TSD are (i) to deal with the design complexity, the entire design space is divided into subspaces with a tree-like structure; it reduces the computation and facilitates experts' involvement in the solving process. (ii) A human-intervention interface is developed to allow experts' involvement in avoiding local optimums and accelerating convergence. To validate the proposed algorithm, the layout design of one-space station is formulated as a multi-disciplinary design problem, the developed algorithm is programmed and executed, and the result is compared with those from other two algorithms; it has illustrated the superior performance of the proposed EGEA-TSD.

  5. Evaluation of models generated via hybrid evolutionary algorithms ...

    African Journals Online (AJOL)

    2016-04-02

    Apr 2, 2016 ... Evaluation of models generated via hybrid evolutionary algorithms for the prediction of Microcystis ... evolutionary algorithms (HEA) proved to be highly applica- ble to the hypertrophic reservoirs of South Africa. .... discovered and optimised using a large-scale parallel computational device and relevant soft-.

  6. Fuzzy Multi-objective Linear Programming Approach

    Directory of Open Access Journals (Sweden)

    Amna Rehmat

    2007-07-01

    Full Text Available Traveling salesman problem (TSP is one of the challenging real-life problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multi-objective linear programming, an amalgamation of fuzzy logic and multi-objective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multi-objective linear programming. An example of TSP with multiple objectives and vague parameters is discussed.

  7. Probing optimal measurement configuration for optical scatterometry by the multi-objective genetic algorithm

    Science.gov (United States)

    Chen, Xiuguo; Gu, Honggang; Jiang, Hao; Zhang, Chuanwei; Liu, Shiyuan

    2018-04-01

    Measurement configuration optimization (MCO) is a ubiquitous and important issue in optical scatterometry, whose aim is to probe the optimal combination of measurement conditions, such as wavelength, incidence angle, azimuthal angle, and/or polarization directions, to achieve a higher measurement precision for a given measuring instrument. In this paper, the MCO problem is investigated and formulated as a multi-objective optimization problem, which is then solved by the multi-objective genetic algorithm (MOGA). The case study on the Mueller matrix scatterometry for the measurement of a Si grating verifies the feasibility of the MOGA in handling the MCO problem in optical scatterometry by making a comparison with the Monte Carlo simulations. Experiments performed at the achieved optimal measurement configuration also show good agreement between the measured and calculated best-fit Mueller matrix spectra. The proposed MCO method based on MOGA is expected to provide a more general and practical means to solve the MCO problem in the state-of-the-art optical scatterometry.

  8. Intersection signal control multi-objective optimization based on genetic algorithm

    Directory of Open Access Journals (Sweden)

    Zhanhong Zhou

    2014-04-01

    Full Text Available A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at an intersection. The optimization method combined the Paramics microscopic traffic simulation software, Comprehensive Modal Emissions Model (CMEM, and genetic algorithm. An intersection in Haizhu District, Guangzhou, was taken for a case study. The result of the case study shows the optimal timing scheme obtained from this method is better than the Webster timing scheme.

  9. A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

    Directory of Open Access Journals (Sweden)

    M. Njah

    2017-06-01

    Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

  10. Global shape optimization of airfoil using multi-objective genetic algorithm

    International Nuclear Information System (INIS)

    Lee, Ju Hee; Lee, Sang Hwan; Park, Kyoung Woo

    2005-01-01

    The shape optimization of an airfoil has been performed for an incompressible viscous flow. In this study, Pareto frontier sets, which are global and non-dominated solutions, can be obtained without various weighting factors by using the multi-objective genetic algorithm. An NACA0012 airfoil is considered as a baseline model, and the profile of the airfoil is parameterized and rebuilt with four Bezier curves. Two curves, from leading to maximum thickness, are composed of five control points and the rest, from maximum thickness to tailing edge, are composed of four control points. There are eighteen design variables and two objective functions such as the lift and drag coefficients. A generation is made up of forty-five individuals. After fifteenth evolutions, the Pareto individuals of twenty can be achieved. One Pareto, which is the best of the reduction of the drag force, improves its drag to 13% and lift-drag ratio to 2%. Another Pareto, however, which is focused on increasing the lift force, can improve its lift force to 61%, while sustaining its drag force, compared to those of the baseline model

  11. Global shape optimization of airfoil using multi-objective genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Ju Hee; Lee, Sang Hwan [Hanyang Univ., Seoul (Korea, Republic of); Park, Kyoung Woo [Hoseo Univ., Asan (Korea, Republic of)

    2005-10-01

    The shape optimization of an airfoil has been performed for an incompressible viscous flow. In this study, Pareto frontier sets, which are global and non-dominated solutions, can be obtained without various weighting factors by using the multi-objective genetic algorithm. An NACA0012 airfoil is considered as a baseline model, and the profile of the airfoil is parameterized and rebuilt with four Bezier curves. Two curves, from leading to maximum thickness, are composed of five control points and the rest, from maximum thickness to tailing edge, are composed of four control points. There are eighteen design variables and two objective functions such as the lift and drag coefficients. A generation is made up of forty-five individuals. After fifteenth evolutions, the Pareto individuals of twenty can be achieved. One Pareto, which is the best of the reduction of the drag force, improves its drag to 13% and lift-drag ratio to 2%. Another Pareto, however, which is focused on increasing the lift force, can improve its lift force to 61%, while sustaining its drag force, compared to those of the baseline model.

  12. Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA

    International Nuclear Information System (INIS)

    Khalili-Damghani, Kaveh; Amiri, Maghsoud

    2012-01-01

    In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto front of binary-state MORAP is generated using an efficient epsilon-constraint method. In order to test the quality of generated non-dominated solutions in this module, a multi-start partial bound enumeration algorithm is also proposed for MORAP. The performance of both procedures is compared using different metrics on well-known benchmark instance. The statistical analysis represents that not only the proposed efficient epsilon-constraint method outperform the multi-start partial bound enumeration algorithm but also it improves the founded upper bound of benchmark instance. Then, in second module, a DEA model is supplied to prune the generated non-dominated solutions of efficient epsilon-constraint method. This helps reduction of non-dominated solutions in a systematic manner and eases the decision making process for practical implementations. - Highlights: ► A procedure based on efficient epsilon-constraint method and DEA was proposed for solving MORAP. ► The performance of proposed procedure was compared with a multi-start PBEA. ► Methods were statistically compared using multi-objective metrics.

  13. Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    L. DJEROU,

    2012-01-01

    Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

  14. A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

    Directory of Open Access Journals (Sweden)

    Yu Lei

    2014-01-01

    Full Text Available A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

  15. Parallel Evolutionary Optimization Algorithms for Peptide-Protein Docking

    Science.gov (United States)

    Poluyan, Sergey; Ershov, Nikolay

    2018-02-01

    In this study we examine the possibility of using evolutionary optimization algorithms in protein-peptide docking. We present the main assumptions that reduce the docking problem to a continuous global optimization problem and provide a way of using evolutionary optimization algorithms. The Rosetta all-atom force field was used for structural representation and energy scoring. We describe the parallelization scheme and MPI/OpenMP realization of the considered algorithms. We demonstrate the efficiency and the performance for some algorithms which were applied to a set of benchmark tests.

  16. Multi-objective optimization and exergetic-sustainability of an irreversible nano scale Braysson cycle operating with Ma

    Directory of Open Access Journals (Sweden)

    Mohammad H. Ahmadi

    2016-06-01

    Full Text Available Nano technology is developed in this decade and changes the way of life. Moreover, developing nano technology has effect on the performance of the materials and consequently improves the efficiency and robustness of them. So, nano scale thermal cycles will be probably engaged in the near future. In this paper, a nano scale irreversible Braysson cycle is studied thermodynamically for optimizing the performance of the Braysson cycle. In the aforementioned cycle an ideal Maxwell–Boltzmann gas is used as a working fluid. Furthermore, three different plans are used for optimizing with multi-objectives; though, the outputs of the abovementioned plans are assessed autonomously. Throughout the first plan, with the purpose of maximizing the ecological coefficient of performance and energy efficiency of the system, multi-objective optimization algorithms are used. Furthermore, in the second plan, two objective functions containing the ecological coefficient of performance and the dimensionless Maximum available work are maximized synchronously by utilizing multi-objective optimization approach. Finally, throughout the third plan, three objective functions involving the dimensionless Maximum available work, the ecological coefficient of performance and energy efficiency of the system are maximized synchronously by utilizing multi-objective optimization approach. The multi-objective evolutionary approach based on the non-dominated sorting genetic algorithm approach is used in this research. Making a decision is performed by three different decision makers comprising linear programming approaches for multidimensional analysis of preference and an approach for order of preference by comparison with ideal answer and Bellman–Zadeh. Lastly, analysis of error is employed to determine deviation of the outcomes gained from each plan.

  17. Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography.

    Science.gov (United States)

    Picek, Stjepan; Carlet, Claude; Guilley, Sylvain; Miller, Julian F; Jakobovic, Domagoj

    2016-01-01

    The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

  18. A brief introduction to continuous evolutionary optimization

    CERN Document Server

    Kramer, Oliver

    2014-01-01

    Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel ...

  19. Multi-objective thermodynamic optimization of combined Brayton and inverse Brayton cycles using genetic algorithms

    International Nuclear Information System (INIS)

    Besarati, S.M.; Atashkari, K.; Jamali, A.; Hajiloo, A.; Nariman-zadeh, N.

    2010-01-01

    This paper presents a simultaneous optimization study of two outputs performance of a previously proposed combined Brayton and inverse Brayton cycles. It has been carried out by varying the upper cycle pressure ratio, the expansion pressure of the bottom cycle and using variable, above atmospheric, bottom cycle inlet pressure. Multi-objective genetic algorithms are used for Pareto approach optimization of the cycle outputs. The two important conflicting thermodynamic objectives that have been considered in this work are net specific work (w s ) and thermal efficiency (η th ). It is shown that some interesting features among optimal objective functions and decision variables involved in the Baryton and inverse Brayton cycles can be discovered consequently.

  20. Evolutionary Algorithms for Boolean Queries Optimization

    Czech Academy of Sciences Publication Activity Database

    Húsek, Dušan; Snášel, Václav; Neruda, Roman; Owais, S.S.J.; Krömer, P.

    2006-01-01

    Roč. 3, č. 1 (2006), s. 15-20 ISSN 1790-0832 R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary algorithms * genetic algorithms * information retrieval * Boolean query Subject RIV: BA - General Mathematics

  1. A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

    Science.gov (United States)

    Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

    2018-04-01

    Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods

  2. A new multi objective optimization model for designing a green supply chain network under uncertainty

    Directory of Open Access Journals (Sweden)

    Mohammad Mahdi Saffar

    2015-01-01

    Full Text Available Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ε-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE is applied.

  3. Multi-objective optimum design of fast tool servo based on improved differential evolution algorithm

    International Nuclear Information System (INIS)

    Zhu, Zhiwei; Zhou, Xiaoqin; Liu, Qiang; Zhao, Shaoxin

    2011-01-01

    The flexure-based mechanism is a promising realization of fast tool servo (FTS), and the optimum determination of flexure hinge parameters is one of the most important elements in the FTS design. This paper presents a multi-objective optimization approach to optimizing the dimension and position parameters of the flexure-based mechanism, which is based on the improved differential evolution algorithm embedding chaos and nonlinear simulated anneal algorithm. The results of optimum design show that the proposed algorithm has excellent performance and a well-balanced compromise is made between two conflicting objectives, the stroke and natural frequency of the FTS mechanism. The validation tests based on finite element analysis (FEA) show good agreement with the results obtained by using the proposed theoretical algorithm of this paper. Finally, a series of experimental tests are conducted to validate the design process and assess the performance of the FTS mechanism. The designed FTS reaches up to a stroke of 10.25 μm with at least 2 kHz bandwidth. Both of the FEA and experimental results demonstrate that the parameters of the flexure-based mechanism determined by the proposed approaches can achieve the specified performance and the proposed approach is suitable for the optimum design of FTS mechanism and of excellent performances

  4. Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

    Science.gov (United States)

    Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

    2016-01-01

    Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

  5. EvAg: A Scalable Peer-to-Peer Evolutionary Algorithm

    NARCIS (Netherlands)

    Laredo, J.L.J.; Eiben, A.E.; van Steen, M.R.; Merelo, J.J.

    2010-01-01

    This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping protocol and where the evolutionary operators act exclusively within the local neighborhoods. This makes the algorithm inherently suited for parallel execution in a

  6. The System of Simulation and Multi-objective Optimization for the Roller Kiln

    Science.gov (United States)

    Huang, He; Chen, Xishen; Li, Wugang; Li, Zhuoqiu

    It is somewhat a difficult researching problem, to get the building parameters of the ceramic roller kiln simulation model. A system integrated of evolutionary algorithms (PSO, DE and DEPSO) and computational fluid dynamics (CFD), is proposed to solve the problem. And the temperature field uniformity and the environment disruption are studied in this paper. With the help of the efficient parallel calculation, the ceramic roller kiln temperature field uniformity and the NOx emissions field have been researched in the system at the same time. A multi-objective optimization example of the industrial roller kiln proves that the system is of excellent parameter exploration capability.

  7. Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g

    Directory of Open Access Journals (Sweden)

    Guozheng Li

    2018-03-01

    Full Text Available The integration of renewable energies into combined cooling, heating, and power (CCHP systems has become increasingly popular in recent years. However, the optimization of renewable energies integrated CCHP (RECCHP systems (i.e., optimal component configurations is far from being well addressed, especially in isolated mode. This study aims to fill this research gap. A multi-objective optimization model characterizing the system reliability, system cost, and environmental sustainability is constructed. In this model, the objectives include minimization of annual total cost (ATC, carbon dioxide emission (CDE, and loss of energy supply probability (LESP. The decision variables representing the configuration of the RECCHP system include the number of photovoltaic (PV panels and wind turbines (WTs, the tilt angle of PV panels, the height of WTs, the maximum fuel consumption, and the capacity of battery and heat storage tanks (HSTs. The multi-objective model is solved by a multi-objective evolutionary algorithm, namely, the preference-inspired coevolutionary algorithm (PICEA-g, resulting in a set of Pareto optimal (trade-off solutions. Then, a decision-making process is demonstrated, selecting a preferred solution amongst those trade-off solutions by further considering the decision-maker preferences. Furthermore, on the optimization of the RECCHP system, operational strategies (i.e., following electric load, FEL, and following thermal load, FTL are considered, respectively. Experimental results show that the FEL and FTL strategies lead to different optimal configurations. In general, the FTL is recommended in summer and winter, while the FEL is more suitable for spring and autumn. Compared with traditional energy systems, RECCHP has better economic and environmental advantages.

  8. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

    Science.gov (United States)

    Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

    2015-12-01

    For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

  9. Reactive power and voltage control based on general quantum genetic algorithms

    DEFF Research Database (Denmark)

    Vlachogiannis, Ioannis (John); Østergaard, Jacob

    2009-01-01

    This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determines...... techniques such as enhanced GA, multi-objective evolutionary algorithm and particle swarm optimization algorithms, as well as the classical primal-dual interior-point optimal power flow algorithm. The comparison demonstrates the ability of the GQ-GA in reaching more optimal solutions....

  10. Irreversibility analysis for optimization design of plate fin heat exchangers using a multi-objective cuckoo search algorithm

    International Nuclear Information System (INIS)

    Wang, Zhe; Li, Yanzhong

    2015-01-01

    Highlights: • The first application of IMOCS for plate-fin heat exchanger design. • Irreversibility degrees of heat transfer and fluid friction are minimized. • Trade-off of efficiency, total cost and pumping power is achieved. • Both EGM and EDM methods have been compared in the optimization of PFHE. • This study has superiority over other single-objective optimization design. - Abstract: This paper introduces and applies an improved multi-objective cuckoo search (IMOCS) algorithm, a novel met-heuristic optimization algorithm based on cuckoo breeding behavior, for the multi-objective optimization design of plate-fin heat exchangers (PFHEs). A modified irreversibility degree of the PFHE is separated into heat transfer and fluid friction irreversibility degrees which are adopted as two initial objective functions to be minimized simultaneously for narrowing the search scope of the design. The maximization efficiency, minimization of pumping power, and total annual cost are considered final objective functions. Results obtained from a two dimensional normalized Pareto-optimal frontier clearly demonstrate the trade-off between heat transfer and fluid friction irreversibility. Moreover, a three dimensional Pareto-optimal frontier reveals a relationship between efficiency, total annual cost, and pumping power in the PFHE design. Three examples presented here further demonstrate that the presented method is able to obtain optimum solutions with higher accuracy, lower irreversibility, and fewer iterations as compared to the previous methods and single-objective design approaches

  11. Designing optimal degradation tests via multi-objective genetic algorithms

    International Nuclear Information System (INIS)

    Marseguerra, Marzio; Zio, Enrico; Cipollone, Maurizio

    2003-01-01

    The experimental determination of the failure time probability distribution of highly reliable components, such as those used in nuclear and aerospace applications, is intrinsically difficult due to the lack, or scarce significance, of failure data which can be collected during the relatively short test periods. A possibility to overcome this difficulty is to resort to the so-called degradation tests, in which measurements of components' degradation are used to infer the failure time distribution. To design such tests, parameters like the number of tests to be run, their frequency and duration, must be set so as to obtain an accurate estimate of the distribution statistics, under the existing limitations of budget. The optimisation problem which results is a non-linear one. In this work, we propose a method, based on multi-objective genetic algorithms for determining the values of the test parameters which optimise both the accuracy in the estimate of the failure time distribution percentiles and the testing costs. The method has been validated on a degradation model of literature

  12. Synthesis of logic circuits with evolutionary algorithms

    Energy Technology Data Exchange (ETDEWEB)

    JONES,JAKE S.; DAVIDSON,GEORGE S.

    2000-01-26

    In the last decade there has been interest and research in the area of designing circuits with genetic algorithms, evolutionary algorithms, and genetic programming. However, the ability to design circuits of the size and complexity required by modern engineering design problems, simply by specifying required outputs for given inputs has as yet eluded researchers. This paper describes current research in the area of designing logic circuits using an evolutionary algorithm. The goal of the research is to improve the effectiveness of this method and make it a practical aid for design engineers. A novel method of implementing the algorithm is introduced, and results are presented for various multiprocessing systems. In addition to evolving standard arithmetic circuits, work in the area of evolving circuits that perform digital signal processing tasks is described.

  13. Multi-objective evacuation routing optimization for toxic cloud releases

    International Nuclear Information System (INIS)

    Gai, Wen-mei; Deng, Yun-feng; Jiang, Zhong-an; Li, Jing; Du, Yan

    2017-01-01

    This paper develops a model for assessing the risks associated with the evacuation process in response to potential chemical accidents, based on which a multi-objective evacuation routing model for toxic cloud releases is proposed taking into account that the travel speed on each arc will be affected by disaster extension. The objectives of the evacuation routing model are to minimize travel time and individual evacuation risk along a path respectively. Two heuristic algorithms are proposed to solve the multi-objective evacuation routing model. Simulation results show the effectiveness and feasibility of the model and algorithms presented in this paper. And, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency route selection in other cases (fires, nuclear accidents). - Highlights: • A model for assessing and visualizing the risks is developed. • A multi-objective evacuation routing model is proposed for toxic cloud releases. • A modified Dijkstra algorithm is designed to obtain an solution of the model. • Two heuristic algorithms have been developed as the optimization tool.

  14. Multi-Objective Two-Dimensional Truss Optimization by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Harun Alrasyid

    2011-05-01

    Full Text Available During last three decade, many mathematical programming methods have been develop for solving optimization problems. However, no single method has been found to be entirely efficient and robust for the wide range of engineering optimization problems. Most design application in civil engineering involve selecting values for a set of design variables that best describe the behavior and performance of the particular problem while satisfying the requirements and specifications imposed by codes of practice. The introduction of Genetic Algorithm (GA into the field of structural optimization has opened new avenues for research because they have been successful applied while traditional methods have failed. GAs is efficient and broadly applicable global search procedure based on stochastic approach which relies on “survival of the fittest” strategy. GAs are search algorithms that are based on the concepts of natural selection and natural genetics. On this research Multi-objective sizing and configuration optimization of the two-dimensional truss has been conducted using a genetic algorithm. Some preliminary runs of the GA were conducted to determine the best combinations of GA parameters such as population size and probability of mutation so as to get better scaling for rest of the runs. Comparing the results from sizing and sizing– configuration optimization, can obtained a significant reduction in the weight and deflection. Sizing–configuration optimization produces lighter weight and small displacement than sizing optimization. The results were obtained by using a GA with relative ease (computationally and these results are very competitive compared to those obtained from other methods of truss optimization.

  15. Analog Circuit Design Optimization Based on Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Mansour Barari

    2014-01-01

    Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

  16. Evolutionary Algorithms Application Analysis in Biometric Systems

    Directory of Open Access Journals (Sweden)

    N. Goranin

    2010-01-01

    Full Text Available Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authenticationprocess simplification in computer systems has raised significant attention to reliability and efficiency of biometricsystems. Modern biometric systems still face many reliability and efficiency related issues such as reference databasesearch speed, errors while recognizing of biometric information or automating biometric feature extraction. Current scientificinvestigations show that application of evolutionary algorithms may significantly improve biometric systems. In thisarticle we provide a comprehensive review of main scientific research done in sphere of evolutionary algorithm applicationfor biometric system parameter improvement.

  17. Hybridizing Evolutionary Algorithms with Opportunistic Local Search

    DEFF Research Database (Denmark)

    Gießen, Christian

    2013-01-01

    There is empirical evidence that memetic algorithms (MAs) can outperform plain evolutionary algorithms (EAs). Recently the first runtime analyses have been presented proving the aforementioned conjecture rigorously by investigating Variable-Depth Search, VDS for short (Sudholt, 2008). Sudholt...

  18. Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization

    Directory of Open Access Journals (Sweden)

    Weishang Gao

    2013-01-01

    Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.

  19. An encoding technique for multiobjective evolutionary algorithms applied to power distribution system reconfiguration.

    Science.gov (United States)

    Guardado, J L; Rivas-Davalos, F; Torres, J; Maximov, S; Melgoza, E

    2014-01-01

    Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.

  20. Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Myeong Jin Ko

    2015-04-01

    Full Text Available To secure a stable energy supply and bring renewable energy to buildings within a reasonable cost range, a hybrid energy system (HES that integrates both fossil fuel energy systems (FFESs and new and renewable energy systems (NRESs needs to be designed and applied. This paper presents a methodology to optimize a HES consisting of three types of NRESs and six types of FFESs while simultaneously minimizing life cycle cost (LCC, maximizing penetration of renewable energy and minimizing annual greenhouse gas (GHG emissions. An elitist non-dominated sorting genetic algorithm is utilized for multi-objective optimization. As an example, we have designed the optimal configuration and sizing for a HES in an elementary school. The evolution of Pareto-optimal solutions according to the variation in the economic, technical and environmental objective functions through generations is discussed. The pair wise trade-offs among the three objectives are also examined.

  1. Economic emission dispatching with variations of wind power and loads using multi-objective optimization by learning automata

    International Nuclear Information System (INIS)

    Liao, H.L.; Wu, Q.H.; Li, Y.Z.; Jiang, L.

    2014-01-01

    Highlights: • Apply multi-objective optimization by learning automata to power system. • Sequentially dimensional search and state memory are incorporated. • Track dispatch under significant variations of wind power and load demand. • Good performance in terms of accuracy, distribution and computation time. - Abstract: This paper is concerned with using multi-objective optimization by learning automata (MOLA) for economic emission dispatching in the environment where wind power and loads vary. With its capabilities of sequentially dimensional search and state memory, MOLA is able to find accurate solutions while satisfying two objectives: fuel cost coupled with environmental emission and voltage stability. Its searching quality and efficiency are measured using the hypervolume indicator for investigating the quality of Pareto front, and demonstrated by tracking the dispatch solutions under significant variations of wind power and load demand. The simulation studies are carried out on the modified midwestern American electric power system and the IEEE 118-bus test system, in which wind power penetration and load variations present. Evaluated on these two power systems, MOLA is fully compared with multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm II (NSGA-II). The simulation results have shown the superiority of MOLA over NAGA-II and MOEA/D, as it is able to obtain more accurate and widely distributed Pareto fronts. In the dynamic environment where the operation condition of both wind speed and load demand varies, MOLA outperforms the other two algorithms, with respect to the tracking ability and accuracy of the solutions

  2. Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

    Science.gov (United States)

    Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

    2015-04-01

    In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

  3. Portfolio optimization using fundamental indicators based on multi-objective EA

    CERN Document Server

    Silva, Antonio Daniel; Horta, Nuno

    2016-01-01

    This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain s...

  4. Environomic multi-objective optimisation of a district heating network considering centralized and decentralized heat pumps

    International Nuclear Information System (INIS)

    Molyneaux, A.; Leyland, G.; Favrat, D.

    2010-01-01

    Concern for the environment has been steadily growing in recent years, and it is becoming more common to include environmental impact and pollution costs in the design problem along with construction, investment and operating costs. To economically respond to the global environmental problems ahead, progress must be made both on more sustainable technologies and on the design methodology, which needs to adopt a more holistic approach. Heat pumps and, in particular systems integrating heat pumps and cogeneration units, offer a significant potential for greenhouse gas reduction. This paper illustrates the application of a multi-objective and multi-modal evolutionary algorithm to facilitate the design and planning of a district heating network based on a combination of centralized and decentralized heat pumps combined with on-site cogeneration. Comparisons are made with an earlier study based on a single objective environomic optimisation of the same overall model.

  5. A Multi-objective PMU Placement Method Considering Observability and Measurement Redundancy using ABC Algorithm

    Directory of Open Access Journals (Sweden)

    KULANTHAISAMY, A.

    2014-05-01

    Full Text Available This paper presents a Multi- objective Optimal Placement of Phasor Measurement Units (MOPP method in large electric transmission systems. It is proposed for minimizing the number of Phasor Measurement Units (PMUs for complete system observability and maximizing the measurement redundancy of the system, simultaneously. The measurement redundancy means that number of times a bus is able to monitor more than once by PMUs set. A higher level of measurement redundancy can maximize the total system observability and it is desirable for a reliable power system state estimation. Therefore, simultaneous optimization of the two conflicting objectives are performed using a binary coded Artificial Bee Colony (ABC algorithm. The complete observability of the power system is first prepared and then, single line loss contingency condition is considered to the main model. The efficiency of the proposed method is validated on IEEE 14, 30, 57 and 118 bus test systems. The valuable approach of ABC algorithm is demonstrated in finding the optimal number of PMUs and their locations by comparing the performance with earlier works.

  6. A two-stage approach for multi-objective decision making with applications to system reliability optimization

    International Nuclear Information System (INIS)

    Li Zhaojun; Liao Haitao; Coit, David W.

    2009-01-01

    This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.

  7. An Encoding Technique for Multiobjective Evolutionary Algorithms Applied to Power Distribution System Reconfiguration

    Directory of Open Access Journals (Sweden)

    J. L. Guardado

    2014-01-01

    Full Text Available Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2 and the Nondominated Sorting Genetic Algorithm II (NSGA-II. The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.

  8. Evolutionary Algorithm for Optimal Vaccination Scheme

    International Nuclear Information System (INIS)

    Parousis-Orthodoxou, K J; Vlachos, D S

    2014-01-01

    The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease

  9. Economic modeling using evolutionary algorithms : the effect of binary encoding of strategies

    NARCIS (Netherlands)

    Waltman, L.R.; Eck, van N.J.; Dekker, Rommert; Kaymak, U.

    2011-01-01

    We are concerned with evolutionary algorithms that are employed for economic modeling purposes. We focus in particular on evolutionary algorithms that use a binary encoding of strategies. These algorithms, commonly referred to as genetic algorithms, are popular in agent-based computational economics

  10. A Clustal Alignment Improver Using Evolutionary Algorithms

    DEFF Research Database (Denmark)

    Thomsen, Rene; Fogel, Gary B.; Krink, Thimo

    2002-01-01

    Multiple sequence alignment (MSA) is a crucial task in bioinformatics. In this paper we extended previous work with evolutionary algorithms (EA) by using MSA solutions obtained from the wellknown Clustal V algorithm as a candidate solution seed of the initial EA population. Our results clearly show...

  11. Virus evolutionary genetic algorithm for task collaboration of logistics distribution

    Science.gov (United States)

    Ning, Fanghua; Chen, Zichen; Xiong, Li

    2005-12-01

    In order to achieve JIT (Just-In-Time) level and clients' maximum satisfaction in logistics collaboration, a Virus Evolutionary Genetic Algorithm (VEGA) was put forward under double constraints of logistics resource and operation sequence. Based on mathematic description of a multiple objective function, the algorithm was designed to schedule logistics tasks with different due dates and allocate them to network members. By introducing a penalty item, make span and customers' satisfaction were expressed in fitness function. And a dynamic adaptive probability of infection was used to improve performance of local search. Compared to standard Genetic Algorithm (GA), experimental result illustrates the performance superiority of VEGA. So the VEGA can provide a powerful decision-making technique for optimizing resource configuration in logistics network.

  12. A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle

    Science.gov (United States)

    Nejlaoui, Mohamed; Houidi, Ajmi; Affi, Zouhaier; Romdhane, Lotfi

    2017-10-01

    This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

  13. Multi-objective ACO algorithms to minimise the makespan and the total rejection cost on BPMs with arbitrary job weights

    Science.gov (United States)

    Jia, Zhao-hong; Pei, Ming-li; Leung, Joseph Y.-T.

    2017-12-01

    In this paper, we investigate the batch-scheduling problem with rejection on parallel machines with non-identical job sizes and arbitrary job-rejected weights. If a job is rejected, the corresponding penalty has to be paid. Our objective is to minimise the makespan of the processed jobs and the total rejection cost of the rejected jobs. Based on the selected multi-objective optimisation approaches, two problems, P1 and P2, are considered. In P1, the two objectives are linearly combined into one single objective. In P2, the two objectives are simultaneously minimised and the Pareto non-dominated solution set is to be found. Based on the ant colony optimisation (ACO), two algorithms, called LACO and PACO, are proposed to address the two problems, respectively. Two different objective-oriented pheromone matrices and heuristic information are designed. Additionally, a local optimisation algorithm is adopted to improve the solution quality. Finally, simulated experiments are conducted, and the comparative results verify the effectiveness and efficiency of the proposed algorithms, especially on large-scale instances.

  14. The Optimization of the Time-Cost Tradeoff Problem in Projects with Conditional Activities Using of the Multi-Objective Charged System Search Algorithm (SMOCSS

    Directory of Open Access Journals (Sweden)

    M. K. Sharbatdar

    2016-11-01

    Full Text Available Abstract The appropriate planning and scheduling for reaching the project goals in the most economical way is the very basic issue of the project management. As in each project, the project manager must determine the required activities for the implementation of the project and select the best option in the implementation of each of the activities, in a way that the least final cost and time of the project is achieved. Considering the number of activities and selecting options for each of the activities, usually the selection has not one unique solution, but it consists of a set of solutions that are not preferred to each other and are known as Pareto solutions. On the other hand, in some actual projects, there are activities that their implementation options depend on the implementation of the prerequisite activity and are not applicable using all the implementation options, and even in some cases the implementation or the non-implementation of some activities are also dependent on the prerequisite activity implementation. These projects can be introduced as conditional projects. Much researchs have been conducted for acquiring Pareto solution set, using different methods and algorithms, but in all the done tasks the time-cost optimization of conditional projects is not considered. Thus, in the present study the concept of conditional network is defined along with some practical examples, then an appropriate way to illustrate these networks and suitable time-cost formulation of these are presented. Finally, for some instances of conditional activity networks, conditional project time-cost optimization conducted multi-objectively using known meta-heuristic algorithms such as multi-objective genetic algorithm, multi-objective particle swarm algorithm and multi-objective charged system search algorithm.

  15. MultiSeq: unifying sequence and structure data for evolutionary analysis

    Directory of Open Access Journals (Sweden)

    Wright Dan

    2006-08-01

    Full Text Available Abstract Background Since the publication of the first draft of the human genome in 2000, bioinformatic data have been accumulating at an overwhelming pace. Currently, more than 3 million sequences and 35 thousand structures of proteins and nucleic acids are available in public databases. Finding correlations in and between these data to answer critical research questions is extremely challenging. This problem needs to be approached from several directions: information science to organize and search the data; information visualization to assist in recognizing correlations; mathematics to formulate statistical inferences; and biology to analyze chemical and physical properties in terms of sequence and structure changes. Results Here we present MultiSeq, a unified bioinformatics analysis environment that allows one to organize, display, align and analyze both sequence and structure data for proteins and nucleic acids. While special emphasis is placed on analyzing the data within the framework of evolutionary biology, the environment is also flexible enough to accommodate other usage patterns. The evolutionary approach is supported by the use of predefined metadata, adherence to standard ontological mappings, and the ability for the user to adjust these classifications using an electronic notebook. MultiSeq contains a new algorithm to generate complete evolutionary profiles that represent the topology of the molecular phylogenetic tree of a homologous group of distantly related proteins. The method, based on the multidimensional QR factorization of multiple sequence and structure alignments, removes redundancy from the alignments and orders the protein sequences by increasing linear dependence, resulting in the identification of a minimal basis set of sequences that spans the evolutionary space of the homologous group of proteins. Conclusion MultiSeq is a major extension of the Multiple Alignment tool that is provided as part of VMD, a structural

  16. Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Kuo-Yang Wu

    2013-01-01

    Full Text Available We investigate the optimal scheduling of retrieval jobs for double-deep type Automated Storage and Retrieval Systems (AS/RS in the Flexible Manufacturing System (FMS used in modern industrial production. Three types of evolutionary algorithms, the Genetic Algorithm (GA, the Immune Genetic Algorithm (IGA, and the Particle Swarm Optimization (PSO algorithm, are implemented to obtain the optimal assignments. The objective is to minimize the working distance, that is, the shortest retrieval time travelled by the Storage and Retrieval (S/R machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.

  17. Selection of security system design via games of imperfect information and multi-objective genetic algorithm

    International Nuclear Information System (INIS)

    Lins, Isis Didier; Rêgo, Leandro Chaves; Moura, Márcio das Chagas

    2013-01-01

    This work analyzes the strategic interaction between a defender and an intelligent attacker by means of a game and reliability framework involving a multi-objective approach and imperfect information so as to support decision-makers in choosing efficiently designed security systems. A multi-objective genetic algorithm is used to determine the optimal security system's configurations representing the tradeoff between the probability of a successful defense and the acquisition and operational costs. Games with imperfect information are considered, in which the attacker has limited knowledge about the actual security system. The types of security alternatives are readily observable, but the number of redundancies actually implemented in each security subsystem is not known. The proposed methodology is applied to an illustrative example considering power transmission lines in the Northeast of Brazil, which are often targets for attackers who aims at selling the aluminum conductors. The empirical results show that the framework succeeds in handling this sort of strategic interaction. -- Highlights: ► Security components must have feasible costs and must be reliable. ► The optimal design of security systems considers a multi-objective approach. ► Games of imperfect information enable the choice of non-dominated configurations. ► MOGA, reliability and games support the entire defender's decision process. ► The selection of effective security systems may discourage attacker's actions

  18. Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm

    International Nuclear Information System (INIS)

    Zhou, Jianzhong; Lu, Peng; Li, Yuanzheng; Wang, Chao; Yuan, Liu; Mo, Li

    2016-01-01

    Highlights: • HTWCS system is established while considering uncertainty of wind power. • An enhanced multi-objective bee colony optimization algorithm is proposed. • Some heuristic repairing strategies are designed to handle various constraints. • HTWCS problem with economic/environment objectives is solved by EMOBCO. - Abstract: This paper presents a short-term economic/environmental hydro-thermal-wind complementary scheduling (HTWCS) system considering uncertainty of wind power, as well as various complicated non-linear constraints. HTWCS system is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., economic and environmental criteria. Then an enhanced multi-objective bee colony optimization algorithm (EMOBCO) is proposed to solve this problem, which adopts Elite archive set, adaptive mutation/selection mechanism and local searching strategy to improve global searching ability of standard bee colony optimization (BCO). Especially, a novel constraints-repairing strategy with compressing decision space and a violation-adjustment method are used to handle various hydraulic and electric constraints. Finally, a daily scheduling simulation case of hydro-thermal-wind system is conducted to verify feasibility and effectiveness of the proposed EMOBCO in solving HTWCS problem. The simulation results indicate that the proposed EMOBCO can provide lower economic cost and smaller pollutant emission than other method established recently while considering various complex constraints in HTWCS problem.

  19. Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems.

    Science.gov (United States)

    Kurek, Wojciech; Ostfeld, Avi

    2013-01-30

    A multi-objective methodology utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) linked to EPANET for trading-off pumping costs, water quality, and tanks sizing of water distribution systems is developed and demonstrated. The model integrates variable speed pumps for modeling the pumps operation, two water quality objectives (one based on chlorine disinfectant concentrations and one on water age), and tanks sizing cost which are assumed to vary with location and diameter. The water distribution system is subject to extended period simulations, variable energy tariffs, Kirchhoff's laws 1 and 2 for continuity of flow and pressure, tanks water level closure constraints, and storage-reliability requirements. EPANET Example 3 is employed for demonstrating the methodology on two multi-objective models, which differ in the imposed water quality objective (i.e., either with disinfectant or water age considerations). Three-fold Pareto optimal fronts are presented. Sensitivity analysis on the storage-reliability constraint, its influence on pumping cost, water quality, and tank sizing are explored. The contribution of this study is in tailoring design (tank sizing), pumps operational costs, water quality of two types, and reliability through residual storage requirements, in a single multi-objective framework. The model was found to be stable in generating multi-objective three-fold Pareto fronts, while producing explainable engineering outcomes. The model can be used as a decision tool for both pumps operation, water quality, required storage for reliability considerations, and tank sizing decision-making. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm

    Science.gov (United States)

    Piroozfard, Hamed; Wong, Kuan Yew

    2015-05-01

    The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm.

  1. Multi Objective Optimization Using Genetic Algorithm of a Pneumatic Connector

    Science.gov (United States)

    Salaam, HA; Taha, Zahari; Ya, TMYS Tuan

    2018-03-01

    The concept of sustainability was first introduced by Dr Harlem Brutland in the 1980’s promoting the need to preserve today’s natural environment for the sake of future generations. Based on this concept, John Elkington proposed an approach to measure sustainability known as Triple Bottom Line (TBL). There are three evaluation criteria’s involved in the TBL approach; namely economics, environmental integrity and social equity. In manufacturing industry the manufacturing costs measure the economic sustainability of a company in a long term. Environmental integrity is a measure of the impact of manufacturing activities on the environment. Social equity is complicated to evaluate; but when the focus is at the production floor level, the production operator health can be considered. In this paper, the TBL approach is applied in the manufacturing of a pneumatic nipple hose. The evaluation criteria used are manufacturing costs, environmental impact, ergonomics impact and also energy used for manufacturing. This study involves multi objective optimization by using genetic algorithm of several possible alternatives for material used in the manufacturing of the pneumatic nipple.

  2. Multi-objective optimization of linear multi-state multiple sliding window system

    International Nuclear Information System (INIS)

    Konak, Abdullah; Kulturel-Konak, Sadan; Levitin, Gregory

    2012-01-01

    This paper considers the optimal element sequencing in a linear multi-state multiple sliding window system that consists of n linearly ordered multi-state elements. Each multi-state element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The failure of type i in the system occurs if for any i (1≤i≤I) the cumulative performance of any r i consecutive elements is lower than w i . The element sequence strongly affects the probability of any type of system failure. The sequence that minimizes the probability of certain type of failure can provide high probability of other types of failures. Therefore the optimization problem for the multiple sliding window system is essentially multi-objective. The paper formulates and solves the multi-objective optimization problem for the multiple sliding window systems. A multi-objective Genetic Algorithm is used as the optimization engine. Illustrative examples are presented.

  3. An observation planning algorithm applied to multi-objective astronomical observations and its simulation in COSMOS field

    Science.gov (United States)

    Jin, Yi; Gu, Yonggang; Zhai, Chao

    2012-09-01

    Multi-Object Fiber Spectroscopic sky surveys are now booming, such as LAMOST already built by China, BIGBOSS project put forward by the U.S. Lawrence Berkeley National Lab and GTC (Gran Telescopio Canarias) telescope developed by the United States, Mexico and Spain. They all use or will use this approach and each fiber can be moved within a certain area for one astrology target, so observation planning is particularly important for this Sky Surveys. One observation planning algorithm used in multi-objective astronomical observations is developed. It can avoid the collision and interference between the fiber positioning units in the focal plane during the observation in one field of view, and the interested objects can be ovserved in a limited round with the maximize efficiency. Also, the observation simulation can be made for wide field of view through multi-FOV observation. After the observation planning is built ,the simulation is made in COSMOS field using GTC telescope. Interested galaxies, stars and high-redshift LBG galaxies are selected after the removal of the mask area, which may be bright stars. Then 9 FOV simulation is completed and observation efficiency and fiber utilization ratio for every round are given. Otherwise,allocating a certain number of fibers for background sky, giving different weights for different objects and how to move the FOV to improve the overall observation efficiency are discussed.

  4. An object-oriented and quadrilateral-mesh based solution adaptive algorithm for compressible multi-fluid flows

    Science.gov (United States)

    Zheng, H. W.; Shu, C.; Chew, Y. T.

    2008-07-01

    In this paper, an object-oriented and quadrilateral-mesh based solution adaptive algorithm for the simulation of compressible multi-fluid flows is presented. The HLLC scheme (Harten, Lax and van Leer approximate Riemann solver with the Contact wave restored) is extended to adaptively solve the compressible multi-fluid flows under complex geometry on unstructured mesh. It is also extended to the second-order of accuracy by using MUSCL extrapolation. The node, edge and cell are arranged in such an object-oriented manner that each of them inherits from a basic object. A home-made double link list is designed to manage these objects so that the inserting of new objects and removing of the existing objects (nodes, edges and cells) are independent of the number of objects and only of the complexity of O( 1). In addition, the cells with different levels are further stored in different lists. This avoids the recursive calculation of solution of mother (non-leaf) cells. Thus, high efficiency is obtained due to these features. Besides, as compared to other cell-edge adaptive methods, the separation of nodes would reduce the memory requirement of redundant nodes, especially in the cases where the level number is large or the space dimension is three. Five two-dimensional examples are used to examine its performance. These examples include vortex evolution problem, interface only problem under structured mesh and unstructured mesh, bubble explosion under the water, bubble-shock interaction, and shock-interface interaction inside the cylindrical vessel. Numerical results indicate that there is no oscillation of pressure and velocity across the interface and it is feasible to apply it to solve compressible multi-fluid flows with large density ratio (1000) and strong shock wave (the pressure ratio is 10,000) interaction with the interface.

  5. An evolutionary algorithm for model selection

    Energy Technology Data Exchange (ETDEWEB)

    Bicker, Karl [CERN, Geneva (Switzerland); Chung, Suh-Urk; Friedrich, Jan; Grube, Boris; Haas, Florian; Ketzer, Bernhard; Neubert, Sebastian; Paul, Stephan; Ryabchikov, Dimitry [Technische Univ. Muenchen (Germany)

    2013-07-01

    When performing partial-wave analyses of multi-body final states, the choice of the fit model, i.e. the set of waves to be used in the fit, can significantly alter the results of the partial wave fit. Traditionally, the models were chosen based on physical arguments and by observing the changes in log-likelihood of the fits. To reduce possible bias in the model selection process, an evolutionary algorithm was developed based on a Bayesian goodness-of-fit criterion which takes into account the model complexity. Starting from systematically constructed pools of waves which contain significantly more waves than the typical fit model, the algorithm yields a model with an optimal log-likelihood and with a number of partial waves which is appropriate for the number of events in the data. Partial waves with small contributions to the total intensity are penalized and likely to be dropped during the selection process, as are models were excessive correlations between single waves occur. Due to the automated nature of the model selection, a much larger part of the model space can be explored than would be possible in a manual selection. In addition the method allows to assess the dependence of the fit result on the fit model which is an important contribution to the systematic uncertainty.

  6. COOMA: AN OBJECT-ORIENTED STOCHASTIC OPTIMIZATION ALGORITHM

    Directory of Open Access Journals (Sweden)

    Stanislav Alexandrovich Tavridovich

    2017-09-01

    Full Text Available Stochastic optimization methods such as genetic algorithm, particle swarm optimization algorithm, and others are successfully used to solve optimization problems. They are all based on similar ideas and need minimal adaptation when being implemented. But several factors complicate the application of stochastic search methods in practice: multimodality of the objective function, optimization with constraints, finding the best parameter configuration of the algorithm, the increasing of the searching space, etc. This paper proposes a new Cascade Object Optimization and Modification Algorithm (COOMA which develops the best ideas of known stochastic optimization methods and can be applied to a wide variety of real-world problems described in the terms of object-oriented models with practically any types of parameters, variables, and associations between objects. The objects of different classes are organized in pools and pools form the hierarchical structure according to the associations between classes. The algorithm is also executed according to the pool structure: the methods of the upper-level pools before changing their objects call the analogous methods of all their subpools. The algorithm starts with initialization step and then passes through a number of iterations during which the objects are modified until the stop criteria are satisfied. The objects are modified using movement, replication and mutation operations. Two-level version of COOMA realizes a built-in self-adaptive mechanism. The optimization statistics for a number of test problems shows that COOMA is able to solve multi-level problems (with objects of different associated classes, problems with multimodal fitness functions and systems of constraints. COOMA source code on Java is available on request.

  7. Hybrid Projected Gradient-Evolutionary Search Algorithm for Mixed Integer Nonlinear Optimization Problems

    National Research Council Canada - National Science Library

    Homaifar, Abdollah; Esterline, Albert; Kimiaghalam, Bahram

    2005-01-01

    The Hybrid Projected Gradient-Evolutionary Search Algorithm (HPGES) algorithm uses a specially designed evolutionary-based global search strategy to efficiently create candidate solutions in the solution space...

  8. Enhanced Multi-Objective Energy Optimization by a Signaling Method

    OpenAIRE

    Soares, João; Borges, Nuno; Vale, Zita; Oliveira, P.B.

    2016-01-01

    In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensi...

  9. The Formation of Optimal Portfolio of Mutual Shares Funds using Multi-Objective Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Yandra Arkeman

    2013-09-01

    Full Text Available Investments in financial assets have become a trend in the globalization era, especially the investment in mutual fund shares. Investors who want to invest in stock mutual funds can set up an investment portfolio in order to generate a minimal risk and maximum return. In this study the authors used the Multi-Objective Genetic Algorithm Non-dominated Sorting II (MOGA NSGA-II technique with the Markowitz portfolio principle to find the best portfolio from several mutual funds. The data used are 10 company stock mutual funds with a period of 12 months, 24 months and 36 months. The genetic algorithm parameters used are crossover probability of 0.65, mutation probability of 0.05, Generation 400 and a population numbering 20 individuals. The study produced a combination of the best portfolios for the period of 24 months with a computing time of 63,289 seconds.

  10. The optimal algorithm for Multi-source RS image fusion.

    Science.gov (United States)

    Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

    2016-01-01

    In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.

  11. Connected Component Model for Multi-Object Tracking.

    Science.gov (United States)

    He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

    2016-08-01

    In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

  12. Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants

    International Nuclear Information System (INIS)

    Ahmadi, Pouria; Dincer, Ibrahim; Rosen, Marc A.

    2011-01-01

    A comprehensive exergy, exergoeconomic and environmental impact analysis and optimization is reported of several combined cycle power plants (CCPPs). In the first part, thermodynamic analyses based on energy and exergy of the CCPPs are performed, and the effect of supplementary firing on the natural gas-fired CCPP is investigated. The latter step includes the effect of supplementary firing on the performance of bottoming cycle and CO 2 emissions, and utilizes the first and second laws of thermodynamics. In the second part, a multi-objective optimization is performed to determine the 'best' design parameters, accounting for exergetic, economic and environmental factors. The optimization considers three objective functions: CCPP exergy efficiency, total cost rate of the system products and CO 2 emissions of the overall plant. The environmental impact in terms of CO 2 emissions is integrated with the exergoeconomic objective function as a new objective function. The results of both exergy and exergoeconomic analyses show that the largest exergy destructions occur in the CCPP combustion chamber, and that increasing the gas turbine inlet temperature decreases the CCPP cost of exergy destruction. The optimization results demonstrates that CO 2 emissions are reduced by selecting the best components and using a low fuel injection rate into the combustion chamber. -- Highlights: → Comprehensive thermodynamic modeling of a combined cycle power plant. → Exergy, economic and environmental analyses of the system. → Investigation of the role of multiobjective exergoenvironmental optimization as a tool for more environmentally-benign design.

  13. Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

    International Nuclear Information System (INIS)

    Punnathanam, Varun; Kotecha, Prakash

    2017-01-01

    Highlights: • Efficient multi-objective optimization algorithm F-YYPO demonstrated. • Three Stirling engine applications with a total of eight cases. • Improvements in the objective function values of up to 30%. • Superior to the popularly used gamultiobj of MATLAB. • F-YYPO has extremely low time complexity. - Abstract: In this work, we demonstrate the performance of Front-based Yin-Yang-Pair Optimization (F-YYPO) to solve multi-objective problems related to Stirling engine systems. The performance of F-YYPO is compared with that of (i) a recently proposed multi-objective optimization algorithm (Multi-Objective Grey Wolf Optimizer) and (ii) an algorithm popularly employed in literature due to its easy accessibility (MATLAB’s inbuilt multi-objective Genetic Algorithm function: gamultiobj). We consider three Stirling engine based optimization problems: (i) the solar-dish Stirling engine system which considers objectives of output power, thermal efficiency and rate of entropy generation; (ii) Stirling engine thermal model which considers the associated irreversibility of the cycle with objectives of output power, thermal efficiency and pressure drop; and finally (iii) an experimentally validated polytropic finite speed thermodynamics based Stirling engine model also with objectives of output power and pressure drop. We observe F-YYPO to be significantly more effective as compared to its competitors in solving the problems, while requiring only a fraction of the computational time required by the other algorithms.

  14. Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

    Science.gov (United States)

    Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

    2006-06-13

    A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

  15. The concept of ageing in evolutionary algorithms: Discussion and inspirations for human ageing.

    Science.gov (United States)

    Dimopoulos, Christos; Papageorgis, Panagiotis; Boustras, George; Efstathiades, Christodoulos

    2017-04-01

    This paper discusses the concept of ageing as this applies to the operation of Evolutionary Algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. Evolutionary Algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of Evolutionary Algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of Evolutionary Algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Constrained multi-objective optimization of radial expanders in organic Rankine cycles by firefly algorithm

    International Nuclear Information System (INIS)

    Bahadormanesh, Nikrouz; Rahat, Shayan; Yarali, Milad

    2017-01-01

    Highlights: • A multi-objective optimization for radial expander in Organic Rankine Cycles is implemented. • By using firefly algorithm, Pareto front based on the size of turbine and thermal efficiency is produced. • Tension and vibration constrains have a significant effect on optimum design points. - Abstract: Organic Rankine Cycles are viable energy conversion systems in sustainable energy systems due to their compatibility with low-temperature heat sources. In the present study, one dimensional model of radial expanders in conjunction with a thermodynamic model of organic Rankine cycles is prepared. After verification, by defining thermal efficiency of the cycle and size parameter of a radial turbine as the objective functions, a multi-objective optimization was conducted regarding tension and vibration constraints for 4 different organic working fluids (R22, R245fa, R236fa and N-Pentane). In addition to mass flow rate, evaporator temperature, maximum pressure of cycle and turbo-machinery design parameters are selected as the decision variables. Regarding Pareto fronts, by a little increase in size of radial expanders, it is feasible to reach high efficiency. Moreover, by assessing the distribution of decision variables, the variables that play a major role in trending between the objective functions are found. Effects of mechanical and vibration constrains on optimum decision variables are investigated. The results of optimization can be considered as an initial values for design of radial turbines for Organic Rankine Cycles.

  17. Scalable multi-objective control for large scale water resources systems under uncertainty

    Science.gov (United States)

    Giuliani, Matteo; Quinn, Julianne; Herman, Jonathan; Castelletti, Andrea; Reed, Patrick

    2016-04-01

    The use of mathematical models to support the optimal management of environmental systems is rapidly expanding over the last years due to advances in scientific knowledge of the natural processes, efficiency of the optimization techniques, and availability of computational resources. However, undergoing changes in climate and society introduce additional challenges for controlling these systems, ultimately motivating the emergence of complex models to explore key causal relationships and dependencies on uncontrolled sources of variability. In this work, we contribute a novel implementation of the evolutionary multi-objective direct policy search (EMODPS) method for controlling environmental systems under uncertainty. The proposed approach combines direct policy search (DPS) with hierarchical parallelization of multi-objective evolutionary algorithms (MOEAs) and offers a threefold advantage: the DPS simulation-based optimization can be combined with any simulation model and does not add any constraint on modeled information, allowing the use of exogenous information in conditioning the decisions. Moreover, the combination of DPS and MOEAs prompts the generation or Pareto approximate set of solutions for up to 10 objectives, thus overcoming the decision biases produced by cognitive myopia, where narrow or restrictive definitions of optimality strongly limit the discovery of decision relevant alternatives. Finally, the use of large-scale MOEAs parallelization improves the ability of the designed solutions in handling the uncertainty due to severe natural variability. The proposed approach is demonstrated on a challenging water resources management problem represented by the optimal control of a network of four multipurpose water reservoirs in the Red River basin (Vietnam). As part of the medium-long term energy and food security national strategy, four large reservoirs have been constructed on the Red River tributaries, which are mainly operated for hydropower

  18. Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

    Directory of Open Access Journals (Sweden)

    Kiran Teeparthi

    2017-04-01

    Full Text Available In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

  19. Multi-objective Optimization of Pulsed Gas Metal Arc Welding Process Using Neuro NSGA-II

    Science.gov (United States)

    Pal, Kamal; Pal, Surjya K.

    2018-05-01

    Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

  20. Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network

    DEFF Research Database (Denmark)

    Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar

    2013-01-01

    Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines...... and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four...... well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable...

  1. Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2017-09-01

    Full Text Available The dyeing of textile materials is the most critical process in cloth production because of the strict technological requirements. In addition to the technical aspect, there have been increasing concerns over how to minimize the negative environmental impact of the dyeing industry. The emissions of pollutants are mainly caused by frequent cleaning operations which are necessary for initializing the dyeing equipment, as well as idled production capacity which leads to discharge of unconsumed chemicals. Motivated by these facts, we propose a methodology to reduce the pollutant emissions by means of systematic production scheduling. Firstly, we build a three-objective scheduling model that incorporates both the traditional tardiness objective and the environmentally-related objectives. A mixed-integer programming formulation is also provided to accurately define the problem. Then, we present a novel solution method for the sustainable scheduling problem, namely, a multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy (MOGA-TIG. Finally, we conduct extensive computational experiments to investigate the actual performance of the MOGA-TIG. Based on a fair comparison with two state-of-the-art multi-objective optimizers, it is concluded that the MOGA-TIG is able to achieve satisfactory solution quality within tight computational time budget for the studied scheduling problem.

  2. Research and Setting the Modified Algorithm "Predator-Prey" in the Problem of the Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    A. P. Karpenko

    2016-01-01

    Full Text Available We consider a class of algorithms for multi-objective optimization - Pareto-approximation algorithms, which suppose a preliminary building of finite-dimensional approximation of a Pareto set, thereby also a Pareto front of the problem. The article gives an overview of population and non-population algorithms of the Pareto-approximation, identifies their strengths and weaknesses, and presents a canonical algorithm "predator-prey", showing its shortcomings. We offer a number of modifications of the canonical algorithm "predator-prey" with the aim to overcome the drawbacks of this algorithm, present the results of a broad study of the efficiency of these modifications of the algorithm. The peculiarity of the study is the use of the quality indicators of the Pareto-approximation, which previous publications have not used. In addition, we present the results of the meta-optimization of the modified algorithm, i.e. determining the optimal values of some free parameters of the algorithm. The study of efficiency of the modified algorithm "predator-prey" has shown that the proposed modifications allow us to improve the following indicators of the basic algorithm: cardinality of a set of the archive solutions, uniformity of archive solutions, and computation time. By and large, the research results have shown that the modified and meta-optimized algorithm enables achieving exactly the same approximation as the basic algorithm, but with the number of preys being one order less. Computational costs are proportionally reduced.

  3. Multi-objective optimal design of sandwich panels using a genetic algorithm

    Science.gov (United States)

    Xu, Xiaomei; Jiang, Yiping; Pueh Lee, Heow

    2017-10-01

    In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.

  4. MOPSO-based multi-objective TSO planning considering uncertainties

    DEFF Research Database (Denmark)

    Wang, Qi; Zhang, Chunyu; Ding, Yi

    2014-01-01

    factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimizes three objectives simultaneously, namely the probabilistic available transfer capability (PATC), investment...... cost and power outage cost. A two-phase MOPSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity ofPareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach...

  5. Multi-Objective Optimization of Grillages Applying the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Darius Mačiūnas

    2012-01-01

    Full Text Available The article analyzes the optimization of grillage-type foundations seeking for the least possible reactive forces in the poles for a given number of poles and for the least possible bending moments of absolute values in the connecting beams of the grillage. Therefore, we suggest using a compromise objective function (to be minimized that consists of the maximum reactive force arising in all poles and the maximum bending moment of the absolute value in connecting beams; both components include the given weights. The variables of task design are pole positions under connecting beams. The optimization task is solved applying the algorithm containing all the initial data of the problem. Reactive forces and bending moments are calculated using an original program (finite element method is applied. This program is integrated into the optimization algorithm using the “black-box” principle. The “black-box” finite element program sends back the corresponding value of the objective function. Numerical experiments revealed the optimal quantity of points to compute bending moments. The obtained results show a certain ratio of weights in the objective function where the contribution of reactive forces and bending moments to the objective function are equivalent. This solution can serve as a pilot project for more detailed design.Article in Lithuanian

  6. Modelling Evolutionary Algorithms with Stochastic Differential Equations.

    Science.gov (United States)

    Heredia, Jorge Pérez

    2017-11-20

    There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.

  7. Evolutionary multi-objective optimization for software development teams building: a way of obtaining quality in the final product

    Directory of Open Access Journals (Sweden)

    Yasnalla Rivero Peña

    2015-03-01

    Full Text Available (Received: 2015/01/29 - Accepted: 2015/03/25In this research a mathematical model to approach the process of creating software development teams as a discrete multi-objective problem is proposed. The objectives considered are the level of competition and the level of utilization of professionals in the team. Given the complexity of the problem search space, the application of an approximate optimization method is proposed. Specifically, the genetic algorithm NSGA-II based on the concept of Pareto dominance was selected. This method was applied in six different scenarios in order to analyze the quality of the obtained solutions. In general we can say that the method is efficient and gets solutions (assignments of high quality.

  8. Multi-objective optimization design of air distribution of grate cooler by entropy generation minimization and genetic algorithm

    International Nuclear Information System (INIS)

    Shao, Wei; Cui, Zheng; Cheng, Lin

    2016-01-01

    Highlights: • A multi-objective optimization model of air distribution of grate cooler by genetic algorithm is proposed. • Pareto Front is obtained and validated by comparing with operating data. • Optimal schemes are compared and selected by engineering background. • Total power consumption after optimization decreases 61.10%. • Thickness of clinker on three grate plates is thinner. - Abstract: The cooling air distributions of grate cooler exercise a great influence on the clinker cooling efficiency and power consumption of cooling fans. A multi-objective optimization model of air distributions of grate cooler with cross-flow heat exchanger analogy is proposed in this paper. Firstly, thermodynamic and flow models of clinker cooling process is carried out. Then based on entropy generation minimization analysis, modified entropy generation numbers caused by heat transfer and pressure drop are chosen as objective functions respectively which optimized by genetic algorithm. The design variables are superficial velocities of air chambers and thicknesses of clinker layers on different grate plates. A set of Pareto optimal solutions which two objectives are optimized simultaneously is achieved. Scattered distributions of design variables resulting in the conflict between two objectives are brought out. The final optimal air distribution and thicknesses of clinker layers are selected from the Pareto optimal solutions based on power consumption of cooling fans minimization and validated by measurements. Compared with actual operating scheme, the total air volumes of optimized schemes decrease 2.4%, total power consumption of cooling fans decreases 61.1% and the outlet temperature of clinker decreases 122.9 °C which shows a remarkable energy-saving effect on energy consumption.

  9. δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms

    Science.gov (United States)

    Aguirre, Hernán; Sato, Masahiko; Tanaka, Kiyoshi

    In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.

  10. Synthesizing mixed H2/H-infinity dynamic controller using evolutionary algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf; Langballe, A.S.; Wisniewski, Rafal

    2001-01-01

    This paper covers the design of an Evolutionary Algorithm (EA), which should be able to synthesize a mixed H2/H-infinity. It will be shown how a system can be expressed as Matrix Inequalities (MI) and these will then be used in the design of the EA. The main objective is to examine whether a mixed...... H2/H-infinity controller is feasible, and if so, how the optimal mixed controller might befound....

  11. Multi-objective genetic algorithm optimization of 2D- and 3D-Pareto fronts for vibrational quantum processes

    International Nuclear Information System (INIS)

    Gollub, C; De Vivie-Riedle, R

    2009-01-01

    A multi-objective genetic algorithm is applied to optimize picosecond laser fields, driving vibrational quantum processes. Our examples are state-to-state transitions and unitary transformations. The approach allows features of the shaped laser fields and of the excitation mechanisms to be controlled simultaneously with the quantum yield. Within the parameter range accessible to the experiment, we focus on short pulse durations and low pulse energies to optimize preferably robust laser fields. Multidimensional Pareto fronts for these conflicting objectives could be constructed. Comparison with previous work showed that the solutions from Pareto optimizations and from optimal control theory match very well.

  12. Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

    Science.gov (United States)

    Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

    2014-03-01

    Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  13. Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction

    Directory of Open Access Journals (Sweden)

    Jingling Zhang

    2012-01-01

    Full Text Available The multiobjective vehicle routing problem considering customer satisfaction (MVRPCS involves the distribution of orders from several depots to a set of customers over a time window. This paper presents a self-adaptive grid multi-objective quantum evolutionary algorithm (MOQEA for the MVRPCS, which takes into account customer satisfaction as well as travel costs. The degree of customer satisfaction is represented by proposing an improved fuzzy due-time window, and the optimization problem is modeled as a mixed integer linear program. In the MOQEA, nondominated solution set is constructed by the Challenge Cup rules. Moreover, an adaptive grid is designed to achieve the diversity of solution sets; that is, the number of grids in each generation is not fixed but is automatically adjusted based on the distribution of the current generation of nondominated solution set. In the study, the MOQEA is evaluated by applying it to classical benchmark problems. Results of numerical simulation and comparison show that the established model is valid and the MOQEA is effective for MVRPCS.

  14. Improving package structure of object-oriented software using multi-objective optimization and weighted class connections

    Directory of Open Access Journals (Sweden)

    Amarjeet

    2017-07-01

    Full Text Available The software maintenance activities performed without following the original design decisions about the package structure usually deteriorate the quality of software modularization, leading to decay of the quality of the system. One of the main reasons for such structural deterioration is inappropriate grouping of source code classes in software packages. To improve such grouping/modular-structure, previous researchers formulated the software remodularization problem as an optimization problem and solved it using search-based meta-heuristic techniques. These optimization approaches aimed at improving the quality metrics values of the structure without considering the original package design decisions, often resulting into a totally new software modularization. The entirely changed software modularization becomes costly to realize as well as difficult to understand for the developers/maintainers. To alleviate this issue, we propose a multi-objective optimization approach to improve the modularization quality of an object-oriented system with minimum possible movement of classes between existing packages of original software modularization. The optimization is performed using NSGA-II, a widely-accepted multi-objective evolutionary algorithm. In order to ensure minimum modification of original package structure, a new approach of computing class relations using weighted strengths has been proposed here. The weights of relations among different classes are computed on the basis of the original package structure. A new objective function has been formulated using these weighted class relations. This objective function drives the optimization process toward better modularization quality simultaneously ensuring preservation of original structure. To evaluate the results of the proposed approach, a series of experiments are conducted over four real-worlds and two random software applications. The experimental results clearly indicate the effectiveness

  15. A Case Study: Optimal Stage Gauge NetworkUsing Multi Objective Genetic Algorithm

    Science.gov (United States)

    Joo, H. J.; Han, D.; Jung, J.; Kim, H. S.

    2017-12-01

    Recently, the possibility of occurrence of localized strong heavy rainfall due to climate change is increasing and flood damage is also increasing trend in Korea. Therefore we need more precise hydrologic analysis for preparing alternatives or measures for flood reduction by considering climate conditions which we have difficulty in the prediction. To do this, obtaining reliable hydrologic data, for an example, stage data, is very important. However, the existing stage gauge stations are scattered around the country, making it difficult to maintain them in a stable manner, and subsequently hard to acquire the hydrologic data that could be used for reflecting the localized hydrologic characteristics. In order to overcome such restrictions, this paper not only aims to establish a plan to acquire the water stage data in a constant and proper manner by using limited manpower and costs, but also establishes the fundamental technology for acquiring the water level observation data or the stage data. For that, this paper identifies the current status of the stage gauge stations installed in the Chung-Ju dam in Han river, Korea and extract the factors related to the division and characteristics of basins. Then, the obtained factors are used to develop the representative unit hydrograph that shows the characteristics of flow. After that, the data are converted into the probability density function and the stations at individual basins are selected by using the entropy theory. In last step, we establish the optimized stage gauge network by the location of the stage station and grade using the Multi Objective Genetic Algorithm(MOGA) technique that takes into account for the combinations of the number of the stations. It is expected that this paper can help establish an optimal observational network of stage guages as it can be applied usefully not only for protecting against floods in a stable manner, but also for acquiring the hydrologic data in an efficient manner. Keywords

  16. Exploitation of linkage learning in evolutionary algorithms

    CERN Document Server

    Chen, Ying-ping

    2010-01-01

    The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.

  17. A binary mixed integer coded genetic algorithm for multi-objective optimization of nuclear research reactor fuel reloading

    Energy Technology Data Exchange (ETDEWEB)

    Binh, Do Quang [University of Technical Education Ho Chi Minh City (Viet Nam); Huy, Ngo Quang [University of Industry Ho Chi Minh City (Viet Nam); Hai, Nguyen Hoang [Centre for Research and Development of Radiation Technology, Ho Chi Minh City (Viet Nam)

    2014-12-15

    This paper presents a new approach based on a binary mixed integer coded genetic algorithm in conjunction with the weighted sum method for multi-objective optimization of fuel loading patterns for nuclear research reactors. The proposed genetic algorithm works with two types of chromosomes: binary and integer chromosomes, and consists of two types of genetic operators: one working on binary chromosomes and the other working on integer chromosomes. The algorithm automatically searches for the most suitable weighting factors of the weighting function and the optimal fuel loading patterns in the search process. Illustrative calculations are implemented for a research reactor type TRIGA MARK II loaded with the Russian VVR-M2 fuels. Results show that the proposed genetic algorithm can successfully search for both the best weighting factors and a set of approximate optimal loading patterns that maximize the effective multiplication factor and minimize the power peaking factor while satisfying operational and safety constraints for the research reactor.

  18. A binary mixed integer coded genetic algorithm for multi-objective optimization of nuclear research reactor fuel reloading

    International Nuclear Information System (INIS)

    Binh, Do Quang; Huy, Ngo Quang; Hai, Nguyen Hoang

    2014-01-01

    This paper presents a new approach based on a binary mixed integer coded genetic algorithm in conjunction with the weighted sum method for multi-objective optimization of fuel loading patterns for nuclear research reactors. The proposed genetic algorithm works with two types of chromosomes: binary and integer chromosomes, and consists of two types of genetic operators: one working on binary chromosomes and the other working on integer chromosomes. The algorithm automatically searches for the most suitable weighting factors of the weighting function and the optimal fuel loading patterns in the search process. Illustrative calculations are implemented for a research reactor type TRIGA MARK II loaded with the Russian VVR-M2 fuels. Results show that the proposed genetic algorithm can successfully search for both the best weighting factors and a set of approximate optimal loading patterns that maximize the effective multiplication factor and minimize the power peaking factor while satisfying operational and safety constraints for the research reactor.

  19. Effective multi-objective optimization of Stirling engine systems

    International Nuclear Information System (INIS)

    Punnathanam, Varun; Kotecha, Prakash

    2016-01-01

    Highlights: • Multi-objective optimization of three recent Stirling engine models. • Use of efficient crossover and mutation operators for real coded Genetic Algorithm. • Demonstrated supremacy of the strategy over the conventionally used algorithm. • Improvements of up to 29% in comparison to literature results. - Abstract: In this article we demonstrate the supremacy of the Non-dominated Sorting Genetic Algorithm-II with Simulated Binary Crossover and Polynomial Mutation operators for the multi-objective optimization of Stirling engine systems by providing three examples, viz., (i) finite time thermodynamic model, (ii) Stirling engine thermal model with associated irreversibility and (iii) polytropic finite speed based thermodynamics. The finite time thermodynamic model involves seven decision variables and consists of three objectives: output power, thermal efficiency and rate of entropy generation. In comparison to literature, it was observed that the used strategy provides a better Pareto front and leads to improvements of up to 29%. The performance is also evaluated on a Stirling engine thermal model which considers the associated irreversibility of the cycle and consists of three objectives involving eleven decision variables. The supremacy of the suggested strategy is also demonstrated on the experimentally validated polytropic finite speed thermodynamics based Stirling engine model for optimization involving two objectives and ten decision variables.

  20. Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

    Science.gov (United States)

    Cao, Jia; Yan, Zheng; He, Guangyu

    2016-06-01

    This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

  1. 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

    Directory of Open Access Journals (Sweden)

    Seyedeh Elham Eftekharian

    2017-11-01

    Full Text Available Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA. In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm.

  2. Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

    Science.gov (United States)

    Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

    2015-05-01

    A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Conflicting Multi-Objective Compatible Optimization Control

    OpenAIRE

    Xu, Lihong; Hu, Qingsong; Hu, Haigen; Goodman, Erik

    2010-01-01

    Based on ideas developed in addressing practical greenhouse environmental control, we propose a new multi-objective compatible control method. Several detailed algorithms are proposed to meet the requirements of different kinds of problem: 1) A two-layer MOCC framework is presented for problems with a precise model; 2) To deal with situations

  4. Evolutionary algorithms applied to Landau-gauge fixing

    International Nuclear Information System (INIS)

    Markham, J.F.

    1998-01-01

    Current algorithms used to put a lattice gauge configuration into Landau gauge either suffer from the problem of critical slowing-down or involve an additions computational expense to overcome it. Evolutionary Algorithms (EAs), which have been widely applied to other global optimisation problems, may be of use in gauge fixing. Also, being global, they should not suffer from critical slowing-down as do local gradient based algorithms. We apply EA'S and also a Steepest Descent (SD) based method to the problem of Landau Gauge Fixing and compare their performance. (authors)

  5. Improvement of Frequency Fluctuations in Microgrids Using an Optimized Fuzzy P-PID Controller by Modified Multi Objective Gravitational Search Algorithm

    Directory of Open Access Journals (Sweden)

    H. Shayeghi

    2016-12-01

    Full Text Available Microgrids is an new opportunity to reduce the total costs of power generation and supply the energy demands through small-scale power plants such as wind sources, photo voltaic panels, battery banks, fuel cells, etc. Like any power system in micro grid (MG, an unexpected faults or load shifting leads to frequency oscillations. Hence, this paper employs an adaptive fuzzy P-PID controller for frequency control of microgrid and a modified multi objective Chaotic Gravitational Search Algorithm (CGSA in order to find out the optimal setting parameters of the proposed controller. To provide a robust controller design, two non-commensurable objective functions are formulated based on eigenvalues-domain and time-domain and multi objective CGSA algorithm is used to solve them. Moreover, a fuzzy decision method is applied to extract the best and optimal Pareto fronts. The proposed controller is carried out on a MG system under different loading conditions with wind turbine generators, photovoltaic system, flywheel energy, battery storages, diesel generator and electrolyzer. The simulation results revealed that the proposed controller is more stable in comparison with the classical and other types of fuzzy controller.

  6. Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions.

    Science.gov (United States)

    Sweetapple, Christine; Fu, Guangtao; Butler, David

    2014-05-15

    This study investigates the potential of control strategy optimisation for the reduction of operational greenhouse gas emissions from wastewater treatment in a cost-effective manner, and demonstrates that significant improvements can be realised. A multi-objective evolutionary algorithm, NSGA-II, is used to derive sets of Pareto optimal operational and control parameter values for an activated sludge wastewater treatment plant, with objectives including minimisation of greenhouse gas emissions, operational costs and effluent pollutant concentrations, subject to legislative compliance. Different problem formulations are explored, to identify the most effective approach to emissions reduction, and the sets of optimal solutions enable identification of trade-offs between conflicting objectives. It is found that multi-objective optimisation can facilitate a significant reduction in greenhouse gas emissions without the need for plant redesign or modification of the control strategy layout, but there are trade-offs to consider: most importantly, if operational costs are not to be increased, reduction of greenhouse gas emissions is likely to incur an increase in effluent ammonia and total nitrogen concentrations. Design of control strategies for a high effluent quality and low costs alone is likely to result in an inadvertent increase in greenhouse gas emissions, so it is of key importance that effects on emissions are considered in control strategy development and optimisation. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

    Science.gov (United States)

    Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

    2018-05-01

    A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

  8. Variants of Evolutionary Algorithms for Real-World Applications

    CERN Document Server

    Weise, Thomas; Michalewicz, Zbigniew

    2012-01-01

    Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book “Variants of Evolutionary Algorithms for Real-World Applications” aims to promote the practitioner’s view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the predictio...

  9. A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

    Energy Technology Data Exchange (ETDEWEB)

    Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

    2015-07-01

    In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

  10. Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front.

    Science.gov (United States)

    Saborido, Rubén; Ruiz, Ana B; Luque, Mariano

    2017-01-01

    In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA ( global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.

  11. SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z; Folkert, M; Iyengar, P; Zhang, Y; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining

  12. An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

    Directory of Open Access Journals (Sweden)

    Fuqing Zhao

    2016-01-01

    Full Text Available A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.

  13. Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Richard Lamb

    2015-09-01

    Full Text Available Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a “virtual” student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning. Comparison of the STAC-M and the STAC-M with inclusion of the Multiobjective Evolutionary Algorithm shows greater success in solving the Piagetian science-tasks post cognitive retraining with the Multiobjective Evolutionary Algorithm. This illustrates the potential uses of cognitive and neuropsychological computational modeling in educational research. The authors also outline the limitations and assumptions of computational modeling.

  14. Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Kangji Li

    2017-02-01

    Full Text Available Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II, multi-objective particle swarm optimization (MOPSO, the multi-objective genetic algorithm (MOGA and multi-objective differential evolution (MODE, are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

  15. A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

    Directory of Open Access Journals (Sweden)

    Zhiming Song

    2015-01-01

    Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

  16. When do evolutionary algorithms optimize separable functions in parallel?

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Sudholt, Dirk; Witt, Carsten

    2013-01-01

    is that evolutionary algorithms make progress on all subfunctions in parallel, so that optimizing a separable function does not take not much longer than optimizing the hardest subfunction-subfunctions are optimized "in parallel." We show that this is only partially true, already for the simple (1+1) evolutionary...... algorithm ((1+1) EA). For separable functions composed of k Boolean functions indeed the optimization time is the maximum optimization time of these functions times a small O(log k) overhead. More generally, for sums of weighted subfunctions that each attain non-negative integer values less than r = o(log1...

  17. Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Man Ding

    2010-01-01

    Full Text Available In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs. The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs and Swarm Intelligence (SI. In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA and Difference Evolution (DE, and one SI algorithm, namely, Particle Swarm Optimization (PSO, on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.

  18. Analysis for Performance of Symbiosis Co-evolutionary Algorithm

    OpenAIRE

    根路銘, もえ子; 遠藤, 聡志; 山田, 孝治; 宮城, 隼夫; Nerome, Moeko; Endo, Satoshi; Yamada, Koji; Miyagi, Hayao

    2000-01-01

    In this paper, we analyze the behavior of symbiotic evolution algorithm for the N-Queens problem as benchmark problem for search methods in the field of aritificial intelligence. It is shown that this algorithm improves the ability of evolutionary search method. When the problem is solved by Genetic Algorithms (GAs), an ordinal representation is often used as one of gene conversion methods which convert from phenotype to genotype and reconvert. The representation can hinder occurrence of leth...

  19. Computing Convex Coverage Sets for Faster Multi-Objective Coordination

    NARCIS (Netherlands)

    Roijers, D.M.; Whiteson, S.; Oliehoek, F.A.

    2015-01-01

    In this article, we propose new algorithms for multi-objective coordination graphs (MO-CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set (CCS) instead of a Pareto coverage set (PCS). Not only is a CCS a sufficient solution set for a large class of problems,

  20. Convex Coverage Set Methods for Multi-Objective Collaborative Decision Making

    NARCIS (Netherlands)

    Roijers, D.M.; Lomuscio, A.; Scerri, P.; Bazzan, A.; Huhns, M.

    2014-01-01

    My research is aimed at finding efficient coordination methods for multi-objective collaborative multi-agent decision theoretic planning. Key to coordinating efficiently in these settings is exploiting loose couplings between agents. We proposed two algorithms for the case in which the agents need

  1. Multi-objective optimization of an arch dam shape under static loads using an evolutionary game method

    Science.gov (United States)

    Meng, Rui; Cheong, Kang Hao; Bao, Wei; Wong, Kelvin Kian Loong; Wang, Lu; Xie, Neng-gang

    2018-06-01

    This article attempts to evaluate the safety and economic performance of an arch dam under the action of static loads. The geometric description of a crown cantilever section and the horizontal arch ring is presented. A three-objective optimization model of arch dam shape is established based on the arch dam volume, maximum principal tensile stress and total strain energy. The evolutionary game method is then applied to obtain the optimal solution. In the evolutionary game technique, a novel and more efficient exploration method of the game players' strategy space, named the 'sorting partition method under the threshold limit', is presented, with the game profit functions constructed according to both competitive and cooperative behaviour. By way of example, three optimization goals have all shown improvements over the initial solutions. In particular, the evolutionary game method has potentially faster convergence. This demonstrates the preliminary proof of principle of the evolutionary game method.

  2. Multi-objective optimization of inverse planning for accurate radiotherapy

    International Nuclear Information System (INIS)

    Cao Ruifen; Pei Xi; Cheng Mengyun; Li Gui; Hu Liqin; Wu Yican; Jing Jia; Li Guoli

    2011-01-01

    The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set. (authors)

  3. Multi-machine power system stabilizers design using chaotic optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Shayeghi, H., E-mail: hshayeghi@gmail.co [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran, Islamic Republic of); Shayanfar, H.A. [Center of Excellence for Power System Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Jalilzadeh, S.; Safari, A. [Technical Engineering Department, Zanjan University, Zanjan (Iran, Islamic Republic of)

    2010-07-15

    In this paper, a multiobjective design of the multi-machine power system stabilizers (PSSs) using chaotic optimization algorithm (COA) is proposed. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. The PSSs parameters tuning problem is converted to an optimization problem which is solved by a chaotic optimization algorithm based on Lozi map. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed chaotic optimization problem introduces chaos mapping using Lozi map chaotic sequences which increases its convergence rate and resulting precision. Two different objective functions are proposed in this study for the PSSs design problem. The first objective function is the eigenvalues based comprising the damping factor, and the damping ratio of the lightly damped electro-mechanical modes, while the second is the time domain-based multi-objective function. The robustness of the proposed COA-based PSSs (COAPSS) is verified on a multi-machine power system under different operating conditions and disturbances. The results of the proposed COAPSS are demonstrated through eigenvalue analysis, nonlinear time-domain simulation and some performance indices. In addition, the potential and superiority of the proposed method over the classical approach and genetic algorithm is demonstrated.

  4. Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

    Science.gov (United States)

    Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

    2018-04-01

    Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

  5. Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm

    International Nuclear Information System (INIS)

    Torres-Echeverría, A.C.; Martorell, S.; Thompson, H.A.

    2012-01-01

    This paper presents the optimization of design and test policies of safety instrumented systems using MooN voting redundancies by a multi-objective genetic algorithm. The objectives to optimize are the Average Probability of Dangerous Failure on Demand, which represents the system safety integrity, the Spurious Trip Rate and the Lifecycle Cost. In this way safety, reliability and cost are included. This is done by using novel models of time-dependent probability of failure on demand and spurious trip rate, recently published by the authors. These models are capable of delivering the level of modeling detail required by the standard IEC 61508. Modeling includes common cause failure and diagnostic coverage. The Probability of Failure on Demand model also permits to quantify results with changing testing strategies. The optimization is performed using the multi-objective Genetic Algorithm NSGA-II. This allows weighting of the trade-offs between the three objectives and, thus, implementation of safety systems that keep a good balance between safety, reliability and cost. The complete methodology is applied to two separate case studies, one for optimization of system design with redundancy allocation and component selection and another for optimization of testing policies. Both optimization cases are performed for both systems with MooN redundancies and systems with only parallel redundancies. Their results are compared, demonstrating how introducing MooN architectures presents a significant improvement for the optimization process.

  6. Sounds unheard of evolutionary algorithms as creative tools for the contemporary composer

    DEFF Research Database (Denmark)

    Dahlstedt, Palle

    2004-01-01

    Evolutionary algorithms are studied as tools for generating novel musical material in the form of musical scores and synthesized sounds. The choice of genetic representation defines a space of potential music. This space is explored using evolutionary algorithms, in search of useful musical mater...... composed with the tools described in the thesis are presented....

  7. A genetic algorithm for optimizing multi-pole Debye models of tissue dielectric properties

    International Nuclear Information System (INIS)

    Clegg, J; Robinson, M P

    2012-01-01

    Models of tissue dielectric properties (permittivity and conductivity) enable the interactions of tissues and electromagnetic fields to be simulated, which has many useful applications in microwave imaging, radio propagation, and non-ionizing radiation dosimetry. Parametric formulae are available, based on a multi-pole model of tissue dispersions, but although they give the dielectric properties over a wide frequency range, they do not convert easily to the time domain. An alternative is the multi-pole Debye model which works well in both time and frequency domains. Genetic algorithms are an evolutionary approach to optimization, and we found that this technique was effective at finding the best values of the multi-Debye parameters. Our genetic algorithm optimized these parameters to fit to either a Cole–Cole model or to measured data, and worked well over wide or narrow frequency ranges. Over 10 Hz–10 GHz the best fits for muscle, fat or bone were each found for ten dispersions or poles in the multi-Debye model. The genetic algorithm is a fast and effective method of developing tissue models that compares favourably with alternatives such as the rational polynomial fit. (paper)

  8. Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

    DEFF Research Database (Denmark)

    Wang, Yong; Cai, Zixing; Zhou, Yuren

    2009-01-01

    A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

  9. Multi-objective ant algorithm for wireless sensor network positioning

    International Nuclear Information System (INIS)

    Fidanova, S.; Shindarov, M.; Marinov, P.

    2013-01-01

    It is impossible to imagine our modern life without telecommunications. Wireless networks are a part of telecommunications. Wireless sensor networks (WSN) consist of spatially distributed sensors, which communicate in wireless way. This network monitors physical or environmental conditions. The objective is the full coverage of the monitoring region and less energy consumption of the network. The most appropriate approach to solve the problem is metaheuristics. In this paper the full coverage of the area is treated as a constrain. The objectives which are optimized are a minimal number of sensors and energy (lifetime) of the network. We apply multi-objective Ant Colony Optimization to solve this important telecommunication problem. We chose MAX-MIN Ant System approach, because it is proven to converge to the global optima

  10. A Survey of Multi-Objective Sequential Decision-Making

    OpenAIRE

    Roijers, D.M.; Vamplew, P.; Whiteson, S.; Dazeley, R.

    2013-01-01

    Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-obj...

  11. High Fidelity Multi-Objective Design Optimization of a Downscaled Cusped Field Thruster

    Directory of Open Access Journals (Sweden)

    Thomas Fahey

    2017-11-01

    Full Text Available The Cusped Field Thruster (CFT concept has demonstrated significantly improved performance over the Hall Effect Thruster and the Gridded Ion Thruster; however, little is understood about the complexities of the interactions and interdependencies of the geometrical, magnetic and ion beam properties of the thruster. This study applies an advanced design methodology combining a modified power distribution calculation and evolutionary algorithms assisted by surrogate modeling to a multi-objective design optimization for the performance optimization and characterization of the CFT. Optimization is performed for maximization of performance defined by five design parameters (i.e., anode voltage, anode current, mass flow rate, and magnet radii, simultaneously aiming to maximize three objectives; that is, thrust, efficiency and specific impulse. Statistical methods based on global sensitivity analysis are employed to assess the optimization results in conjunction with surrogate models to identify key design factors with respect to the three design objectives and additional performance measures. The research indicates that the anode current and the Outer Magnet Radius have the greatest effect on the performance parameters. An optimal value for the anode current is determined, and a trend towards maximizing anode potential and mass flow rate is observed.

  12. PRODUCT LIFECYCLE OPTIMISATION OF CAR CLIMATE CONTROLS USING ANALYTICAL HIERARCHICAL PROCESS (AHP ANALYSIS AND A MULTI-OBJECTIVE GROUPING GENETIC ALGORITHM (MOGGA

    Directory of Open Access Journals (Sweden)

    MICHAEL J. LEE

    2016-01-01

    Full Text Available A product’s lifecycle performance (e.g. assembly, outsourcing, maintenance and recycling can often be improved through modularity. However, modularisation under different and often conflicting lifecycle objectives is a complex problem that will ultimately require trade-offs. This paper presents a novel multi-objective modularity optimisation framework; the application of which is illustrated through the modularisation of a car climate control system. Central to the framework is a specially designed multi-objective grouping genetic algorithm (MOGGA that is able to generate a whole range of alternative product modularisations. Scenario analysis, using the principles of the analytical hierarchical process (AHP, is then carried out to explore the solution set and choose a suitable modular architecture that optimises the product lifecycle according to the company’s strategic vision.

  13. A hybrid multi-objective evolutionary algorithm approach for ...

    Indian Academy of Sciences (India)

    This paper addresses a fuzzy mixed-integer non-linear programming (FMINLP) model by considering machine-dependent and job-sequence-dependent set-up times that minimize the total completion time,the number of tardy jobs, the total flow time and the machine load variation in the context of unrelated parallel machine ...

  14. A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems

    International Nuclear Information System (INIS)

    Attar, Ahmad; Raissi, Sadigh; Khalili-Damghani, Kaveh

    2017-01-01

    A simulation-based optimization (SBO) method is proposed to handle multi-objective joint availability-redundancy allocation problem (JARAP). Here, there is no emphasis on probability distributions of time to failures and repair times for multi-state multi-component series-parallel configuration under active, cold and hot standby strategies. Under such conditions, estimation of availability is not a trivial task. First, an efficient computer simulation model is proposed to estimate the availability of the aforementioned system. Then, the estimated availability values are used in a repetitive manner as parameter of a two-objective joint availability-redundancy allocation optimization model through SBO mechanism. The optimization model is then solved using two well-known multi-objective evolutionary computation algorithms, i.e., non-dominated sorting genetic algorithm (NSGA-II), and Strength Pareto Evolutionary Algorithm (SPEA2). The proposed SBO approach is tested using non-exponential numerical example with multi-state repairable components. The results are presented and discussed through different demand scenarios under cold and hot standby strategies. Furthermore, performance of NSGA-II and SPEA2 are statistically compared regarding multi-objective accuracy, and diversity metrics. - Highlights: • A Simulation-Based Optimization (SBO) procedure is introduced for JARAP. • The proposed SBO works for any given failure and repair times. • An efficient simulation procedure is developed to estimate availability. • Customized NSGA-II and SPEA2 are proposed to solve the bi-objective JARAP. • Statistical analysis is employed to test the performance of optimization methods.

  15. (MBO) algorithm in multi-reservoir system optimisation

    African Journals Online (AJOL)

    A comparative study of marriage in honey bees optimisation (MBO) algorithm in ... A practical application of the marriage in honey bees optimisation (MBO) ... to those of other evolutionary algorithms, such as the genetic algorithm (GA), ant ...

  16. Methodology, Algorithms, and Emerging Tool for Automated Design of Intelligent Integrated Multi-Sensor Systems

    Directory of Open Access Journals (Sweden)

    Andreas König

    2009-11-01

    Full Text Available The emergence of novel sensing elements, computing nodes, wireless communication and integration technology provides unprecedented possibilities for the design and application of intelligent systems. Each new application system must be designed from scratch, employing sophisticated methods ranging from conventional signal processing to computational intelligence. Currently, a significant part of this overall algorithmic chain of the computational system model still has to be assembled manually by experienced designers in a time and labor consuming process. In this research work, this challenge is picked up and a methodology and algorithms for automated design of intelligent integrated and resource-aware multi-sensor systems employing multi-objective evolutionary computation are introduced. The proposed methodology tackles the challenge of rapid-prototyping of such systems under realization constraints and, additionally, includes features of system instance specific self-correction for sustained operation of a large volume and in a dynamically changing environment. The extension of these concepts to the reconfigurable hardware platform renders so called self-x sensor systems, which stands, e.g., for self-monitoring, -calibrating, -trimming, and -repairing/-healing systems. Selected experimental results prove the applicability and effectiveness of our proposed methodology and emerging tool. By our approach, competitive results were achieved with regard to classification accuracy, flexibility, and design speed under additional design constraints.

  17. Multi-Objective Optimization of Managed Aquifer Recharge.

    Science.gov (United States)

    Fatkhutdinov, Aybulat; Stefan, Catalin

    2018-04-27

    This study demonstrates the utilization of a multi-objective hybrid global/local optimization algorithm for solving managed aquifer recharge (MAR) design problems, in which the decision variables included spatial arrangement of water injection and abstraction wells and time-variant rates of pumping and injection. The objective of the optimization was to maximize the efficiency of the MAR scheme, which includes both quantitative and qualitative aspects. The case study used to demonstrate the capabilities of the proposed approach is based on a published report on designing a real MAR site with defined aquifer properties, chemical groundwater characteristics as well as quality and volumes of injected water. The demonstration problems include steady-state and transient scenarios. The steady-state scenario demonstrates optimization of spatial arrangement of multiple injection and recovery wells, whereas the transient scenario was developed with the purpose of finding optimal regimes of water injection and recovery at a single location. Both problems were defined as multi-objective problems. The scenarios were simulated by applying coupled numerical groundwater flow and solute transport models: MODFLOW-2005 and MT3D-USGS. The applied optimization method was a combination of global - the Non-Dominated Sorting Genetic Algorithm (NSGA-2), and local - the Nelder-Mead Downhill Simplex search algorithms. The analysis of the resulting Pareto optimal solutions led to the discovery of valuable patterns and dependencies between the decision variables, model properties and problem objectives. Additionally, the performance of the traditional global and the hybrid optimization schemes were compared. This article is protected by copyright. All rights reserved.

  18. Multi-objective optimization algorithms for mixed model assembly line balancing problem with parallel workstations

    Directory of Open Access Journals (Sweden)

    Masoud Rabbani

    2016-12-01

    Full Text Available This paper deals with mixed model assembly line (MMAL balancing problem of type-I. In MMALs several products are made on an assembly line while the similarity of these products is so high. As a result, it is possible to assemble several types of products simultaneously without any additional setup times. The problem has some particular features such as parallel workstations and precedence constraints in dynamic periods in which each period also effects on its next period. The research intends to reduce the number of workstations and maximize the workload smoothness between workstations. Dynamic periods are used to determine all variables in different periods to achieve efficient solutions. A non-dominated sorting genetic algorithm (NSGA-II and multi-objective particle swarm optimization (MOPSO are used to solve the problem. The proposed model is validated with GAMS software for small size problem and the performance of the foregoing algorithms is compared with each other based on some comparison metrics. The NSGA-II outperforms MOPSO with respect to some comparison metrics used in this paper, but in other metrics MOPSO is better than NSGA-II. Finally, conclusion and future research is provided.

  19. Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

    Directory of Open Access Journals (Sweden)

    Zhong-Kai Feng

    2017-01-01

    Full Text Available With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

  20. Multi-objective optimization of an industrial penicillin V bioreactor train using non-dominated sorting genetic algorithm.

    Science.gov (United States)

    Lee, Fook Choon; Rangaiah, Gade Pandu; Ray, Ajay Kumar

    2007-10-15

    Bulk of the penicillin produced is used as raw material for semi-synthetic penicillin (such as amoxicillin and ampicillin) and semi-synthetic cephalosporins (such as cephalexin and cefadroxil). In the present paper, an industrial penicillin V bioreactor train is optimized for multiple objectives simultaneously. An industrial train, comprising a bank of identical bioreactors, is run semi-continuously in a synchronous fashion. The fermentation taking place in a bioreactor is modeled using a morphologically structured mechanism. For multi-objective optimization for two and three objectives, the elitist non-dominated sorting genetic algorithm (NSGA-II) is chosen. Instead of a single optimum as in the traditional optimization, a wide range of optimal design and operating conditions depicting trade-offs of key performance indicators such as batch cycle time, yield, profit and penicillin concentration, is successfully obtained. The effects of design and operating variables on the optimal solutions are discussed in detail. Copyright 2007 Wiley Periodicals, Inc.

  1. The multi-objective optimization of the horizontal-axis marine current turbine based on NSGA-II algorithm

    International Nuclear Information System (INIS)

    Zhu, G J; Guo, P C; Luo, X Q; Feng, J J

    2012-01-01

    The present paper describes a hydrodynamic optimization technique for horizontal-axial marine current turbine. The pitch angle distribution is important to marine current turbine. In this paper, the pitch angle distribution curve is parameterized as four control points by Bezier curve method. The coordinates of the four control points are chosen as optimization variables, and the sample space are structured according to the Box-Behnken experimental design method (BBD). Then the power capture coefficient and axial thrust coefficient in design tip-speed ratio is obtained for all the elements in the sample space by CFD numerical simulation. The power capture coefficient and axial thrust are chosen as objective function, and quadratic polynomial regression equations are constructed to fit the relationship between the optimization variables and each objective function according to response surface model. With the obtained quadratic polynomial regression equations as performance prediction model, the marine current turbine is optimized using the NSGA-II multi-objective genetic algorithm, which finally offers an improved marine current turbine.

  2. The (1+λ) evolutionary algorithm with self-adjusting mutation rate

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Witt, Carsten; Gießen, Christian

    2017-01-01

    We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then upd......We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate...... is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the (1 + A) evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the (1 + A) EA finds the optimum in an expected optimization...... time (number of fitness evaluations) of O(nA/log A + n log n). This time is asymptotically smaller than the optimization time of the classic (1 + A) EA. Previous work shows that this performance is best-possible among all A-parallel mutation-based unbiased black-box algorithms. This result shows...

  3. Aerodynamic multi-objective integrated optimization based on principal component analysis

    Directory of Open Access Journals (Sweden)

    Jiangtao HUANG

    2017-08-01

    Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

  4. Performance comparison of some evolutionary algorithms on job shop scheduling problems

    Science.gov (United States)

    Mishra, S. K.; Rao, C. S. P.

    2016-09-01

    Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

  5. Prospective Algorithms for Quantum Evolutionary Computation

    OpenAIRE

    Sofge, Donald A.

    2008-01-01

    This effort examines the intersection of the emerging field of quantum computing and the more established field of evolutionary computation. The goal is to understand what benefits quantum computing might offer to computational intelligence and how computational intelligence paradigms might be implemented as quantum programs to be run on a future quantum computer. We critically examine proposed algorithms and methods for implementing computational intelligence paradigms, primarily focused on ...

  6. A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

    Directory of Open Access Journals (Sweden)

    Qianwang Deng

    2017-01-01

    Full Text Available Flexible job-shop scheduling problem (FJSP is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II for multiobjective FJSP (MO-FJSP with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

  7. A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling.

    Science.gov (United States)

    Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

    2017-01-01

    Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

  8. Multi-objective optimal strategy for generating and bidding in the power market

    International Nuclear Information System (INIS)

    Peng Chunhua; Sun Huijuan; Guo Jianfeng; Liu Gang

    2012-01-01

    Highlights: ► A new benefit/risk/emission comprehensive generation optimization model is established. ► A hybrid multi-objective differential evolution optimization algorithm is designed. ► Fuzzy set theory and entropy weighting method are employed to extract the general best solution. ► The proposed approach of generating and bidding is efficient for maximizing profit and minimizing both risk and emissions. - Abstract: Based on the coordinated interaction between units output and electricity market prices, the benefit/risk/emission comprehensive generation optimization model with objectives of maximal profit and minimal bidding risk and emissions is established. A hybrid multi-objective differential evolution optimization algorithm, which successfully integrates Pareto non-dominated sorting with differential evolution algorithm and improves individual crowding distance mechanism and mutation strategy to avoid premature and unevenly search, is designed to achieve Pareto optimal set of this model. Moreover, fuzzy set theory and entropy weighting method are employed to extract one of the Pareto optimal solutions as the general best solution. Several optimization runs have been carried out on different cases of generation bidding and scheduling. The results confirm the potential and effectiveness of the proposed approach in solving the multi-objective optimization problem of generation bidding and scheduling. In addition, the comparison with the classical optimization algorithms demonstrates the superiorities of the proposed algorithm such as integrality of Pareto front, well-distributed Pareto-optimal solutions, high search speed.

  9. An Endosymbiotic Evolutionary Algorithm for the Hub Location-Routing Problem

    Directory of Open Access Journals (Sweden)

    Ji Ung Sun

    2015-01-01

    Full Text Available We consider a capacitated hub location-routing problem (HLRP which combines the hub location problem and multihub vehicle routing decisions. The HLRP not only determines the locations of the capacitated p-hubs within a set of potential hubs but also deals with the routes of the vehicles to meet the demands of customers. This problem is formulated as a 0-1 mixed integer programming model with the objective of the minimum total cost including routing cost, fixed hub cost, and fixed vehicle cost. As the HLRP has impractically demanding for the large sized problems, we develop a solution method based on the endosymbiotic evolutionary algorithm (EEA which solves hub location and vehicle routing problem simultaneously. The performance of the proposed algorithm is examined through a comparative study. The experimental results show that the proposed EEA can be a viable solution method for the supply chain network planning.

  10. Evolving intelligent vehicle control using multi-objective NEAT

    NARCIS (Netherlands)

    Willigen, W.H. van; Haasdijk, E.; Kester, L.J.H.M.

    2013-01-01

    The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective algorithm based on NEAT and SPEA2 that evolves controllers for such

  11. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    Science.gov (United States)

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  12. Intelligent multi-objective optimization for building energy and comfort management

    Directory of Open Access Journals (Sweden)

    Pervez Hameed Shaikh

    2018-04-01

    Full Text Available The rapid economic and population growth in developing countries, effective and efficient energy usage has turned out to be crucial due to the rising concern of depleting fossil fuels, of which, one-third of primary energy is consumed in buildings and expected to rise by 53% up to 2030. This roaring sector posing a challenge, due to 90% of people spend most of their time in buildings, requires enhanced well-being of indoor environment and living standards. Therefore, building operations require more energy because most of the energy is consumed to make the indoor environment comfortable. Consequently, there is the need of improved energy efficiency to decrease energy consumption in buildings. In relation to this, the primary challenge of building control systems is the energy consumption and comfort level are generally conflicting to each other. Therefore, an important problem of sustainable smart buildings is to effectively manage the energy consumption and comfort and attain the trade-off between the two. Thus, smart buildings are becoming a trend of future construction that facilitates intelligent control in buildings for the fulfillment of occupant’s comfort level. In this study, an intelligent multi-objective system has been developed with evolutionary multi-objective genetic algorithm (MOGA optimization method. The corresponding case study simulation results for the effective management of users’ comfort and energy efficiency have been carried out. The case study results show the management of energy supply for each comfort parameter and maintain high comfort index achieving balance between the energy consumption and comfort level. Keywords: Energy, Buildings, Comfort, Management, Optimization, Trade-off

  13. Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

    International Nuclear Information System (INIS)

    Dong, Feifei; Liu, Yong; Su, Han; Zou, Rui; Guo, Huaicheng

    2015-01-01

    Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to

  14. Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

    Energy Technology Data Exchange (ETDEWEB)

    Dong, Feifei [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Liu, Yong, E-mail: yongliu@pku.edu.cn [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Institute of Water Sciences, Peking University, Beijing 100871 (China); Su, Han [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Zou, Rui [Tetra Tech, Inc., 10306 Eaton Place, Ste 340, Fairfax, VA 22030 (United States); Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed, Kunming 650034 (China); Guo, Huaicheng [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China)

    2015-05-15

    Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to

  15. Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture series

    CERN Multimedia

    Françoise Benz

    2004-01-01

    ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...

  16. Multi-objective optimization in computer networks using metaheuristics

    CERN Document Server

    Donoso, Yezid

    2007-01-01

    Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

  17. Multi-objective optimization of the control strategy of electric vehicle electro-hydraulic composite braking system with genetic algorithm

    Directory of Open Access Journals (Sweden)

    Zhang Fengjiao

    2015-03-01

    Full Text Available Optimization of the control strategy plays an important role in improving the performance of electric vehicles. In order to improve the braking stability and recover the braking energy, a multi-objective genetic algorithm is applied to optimize the key parameters in the control strategy of electric vehicle electro-hydraulic composite braking system. Various limitations are considered in the optimization process, and the optimization results are verified by a software simulation platform of electric vehicle regenerative braking system in typical brake conditions. The results show that optimization objectives achieved a good astringency, and the optimized control strategy can increase the brake energy recovery effectively under the condition of ensuring the braking stability.

  18. Nash evolutionary algorithms : Testing problem size in reconstruction problems in frame structures

    OpenAIRE

    Greiner, D.; Periaux, Jacques; Emperador, J.M.; Galván, B.; Winter, G.

    2016-01-01

    The use of evolutionary algorithms has been enhanced in recent years for solving real engineering problems, where the requirements of intense computational calculations are needed, especially when computational engineering simulations are involved (use of finite element method, boundary element method, etc). The coupling of game-theory concepts in evolutionary algorithms has been a recent line of research which could enhance the efficiency of the optimum design procedure and th...

  19. Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

    Science.gov (United States)

    Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

    2017-04-01

    In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

  20. An Algorithm-Independent Analysis of the Quality of Images Produced Using Multi-Frame Blind Deconvolution Algorithms--Conference Proceedings (Postprint)

    National Research Council Canada - National Science Library

    Matson, Charles; Haji, Alim

    2007-01-01

    Multi-frame blind deconvolution (MFBD) algorithms can be used to generate a deblurred image of an object from a sequence of short-exposure and atmospherically-blurred images of the object by jointly estimating the common object...

  1. Packets Distributing Evolutionary Algorithm Based on PSO for Ad Hoc Network

    Science.gov (United States)

    Xu, Xiao-Feng

    2018-03-01

    Wireless communication network has such features as limited bandwidth, changeful channel and dynamic topology, etc. Ad hoc network has lots of difficulties in accessing control, bandwidth distribution, resource assign and congestion control. Therefore, a wireless packets distributing Evolutionary algorithm based on PSO (DPSO)for Ad Hoc Network is proposed. Firstly, parameters impact on performance of network are analyzed and researched to obtain network performance effective function. Secondly, the improved PSO Evolutionary Algorithm is used to solve the optimization problem from local to global in the process of network packets distributing. The simulation results show that the algorithm can ensure fairness and timeliness of network transmission, as well as improve ad hoc network resource integrated utilization efficiency.

  2. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

    Directory of Open Access Journals (Sweden)

    Fonseca Carlos M

    2010-10-01

    Full Text Available Abstract Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the

  3. Strength Pareto Evolutionary Algorithm using Self-Organizing Data Analysis Techniques

    Directory of Open Access Journals (Sweden)

    Ionut Balan

    2015-03-01

    Full Text Available Multiobjective optimization is widely used in problems solving from a variety of areas. To solve such problems there was developed a set of algorithms, most of them based on evolutionary techniques. One of the algorithms from this class, which gives quite good results is SPEA2, method which is the basis of the proposed algorithm in this paper. Results from this paper are obtained by running these two algorithms on a flow-shop problem.

  4. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

    Science.gov (United States)

    Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

    2017-01-01

    Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

  5. Optimization of Multipurpose Reservoir Operation with Application Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Elahe Fallah Mehdipour

    2012-12-01

    Full Text Available Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research, application of multi-objective particle swarm optimization (MOPSO in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture, recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

  6. International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems

    CERN Document Server

    Dash, Subhransu; Panigrahi, Bijaya

    2015-01-01

      The book is a collection of high-quality peer-reviewed research papers presented in Proceedings of International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES 2014) held at Noorul Islam Centre for Higher Education, Kumaracoil, India. These research papers provide the latest developments in the broad area of use of artificial intelligence and evolutionary algorithms in engineering systems. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

  7. Adaptive Variance Scaling in Continuous Multi-Objective Estimation-of-Distribution Algorithms

    NARCIS (Netherlands)

    P.A.N. Bosman (Peter); D. Thierens (Dirk); D. Thierens (Dirk)

    2007-01-01

    htmlabstractRecent research into single-objective continuous Estimation-of-Distribution Algorithms (EDAs) has shown that when maximum-likelihood estimations are used for parametric distributions such as the normal distribution, the EDA can easily suffer from premature convergence. In this paper we

  8. Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

    International Nuclear Information System (INIS)

    Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

    2015-01-01

    Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

  9. Reduction environmental effects of civil aircraft through multi-objective flight plan optimisation

    International Nuclear Information System (INIS)

    Lee, D S; Gonzalez, L F; Walker, R; Periaux, J; Onate, E

    2010-01-01

    With rising environmental alarm, the reduction of critical aircraft emissions including carbon dioxides (CO 2 ) and nitrogen oxides (NO x ) is one of most important aeronautical problems. There can be many possible attempts to solve such problem by designing new wing/aircraft shape, new efficient engine, etc. The paper rather provides a set of acceptable flight plans as a first step besides replacing current aircrafts. The paper investigates a green aircraft design optimisation in terms of aircraft range, mission fuel weight (CO 2 ) and NO x using advanced Evolutionary Algorithms coupled to flight optimisation system software. Two multi-objective design optimisations are conducted to find the best set of flight plans for current aircrafts considering discretised altitude and Mach numbers without designing aircraft shape and engine types. The objectives of first optimisation are to maximise range of aircraft while minimising NO x with constant mission fuel weight. The second optimisation considers minimisation of mission fuel weight and NO x with fixed aircraft range. Numerical results show that the method is able to capture a set of useful trade-offs that reduce NO x and CO 2 (minimum mission fuel weight).

  10. An Evolutionary Algorithm to Mine High-Utility Itemsets

    Directory of Open Access Journals (Sweden)

    Jerry Chun-Wei Lin

    2015-01-01

    Full Text Available High-utility itemset mining (HUIM is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM of association rules (ARs. In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs based on the binary particle swarm optimization. A maximal pattern (MP-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

  11. Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm

    Science.gov (United States)

    Cui, Xue; Gao, Jian; Feng, Yunbin; Zou, Chenlu; Liu, Huanlei

    2018-01-01

    In this paper, an optimization model with the minimum active power loss and minimum voltage deviation of node and maximum static voltage stability margin as the optimization objective is proposed for the reactive power optimization problems. By defining the index value of reactive power compensation, the optimal reactive power compensation node was selected. The particle swarm optimization algorithm was improved, and the selection pool of global optimal and the global optimal of probability (p-gbest) were introduced. A set of Pareto optimal solution sets is obtained by this algorithm. And by calculating the fuzzy membership value of the pareto optimal solution sets, individuals with the smallest fuzzy membership value were selected as the final optimization results. The above improved algorithm is used to optimize the reactive power of IEEE14 standard node system. Through the comparison and analysis of the results, it has been proven that the optimization effect of this algorithm was very good.

  12. Reinforcement Learning for Online Control of Evolutionary Algorithms

    NARCIS (Netherlands)

    Eiben, A.; Horvath, Mark; Kowalczyk, Wojtek; Schut, Martijn

    2007-01-01

    The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We

  13. Models for Evolutionary Algorithms and Their Applications in System Identification and Control Optimization

    DEFF Research Database (Denmark)

    Ursem, Rasmus Kjær

    population and many generations, which essentially turns the problem into a series of related static problems. To our surprise, the control problem could easily be solved when optimized like this. To further examine this, we compared the EA with a particle swarm and a local search approach, which we...... simulate an evolutionary process where the goal is to evolve solutions by means of crossover, mutation, and selection based on their quality (fitness) with respect to the optimization problem at hand. Evolutionary algorithms (EAs) are highly relevant for industrial applications, because they are capable...... of handling problems with non-linear constraints, multiple objectives, and dynamic components – properties that frequently appear in real-world problems. This thesis presents research in three fundamental areas of EC; fitness function design, methods for parameter control, and techniques for multimodal...

  14. Universal approximators for multi-objective direct policy search in water reservoir management problems: a comparative analysis

    Science.gov (United States)

    Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca

    2014-05-01

    The optimal operation of water resources systems is a wide and challenging problem due to non-linearities in the model and the objectives, high dimensional state-control space, and strong uncertainties in the hydroclimatic regimes. The application of classical optimization techniques (e.g., SDP, Q-learning, gradient descent-based algorithms) is strongly limited by the dimensionality of the system and by the presence of multiple, conflicting objectives. This study presents a novel approach which combines Direct Policy Search (DPS) and Multi-Objective Evolutionary Algorithms (MOEAs) to solve high-dimensional state and control space problems involving multiple objectives. DPS, also known as parameterization-simulation-optimization in the water resources literature, is a simulation-based approach where the reservoir operating policy is first parameterized within a given family of functions and, then, the parameters optimized with respect to the objectives of the management problem. The selection of a suitable class of functions to which the operating policy belong to is a key step, as it might restrict the search for the optimal policy to a subspace of the decision space that does not include the optimal solution. In the water reservoir literature, a number of classes have been proposed. However, many of these rules are based largely on empirical or experimental successes and they were designed mostly via simulation and for single-purpose reservoirs. In a multi-objective context similar rules can not easily inferred from the experience and the use of universal function approximators is generally preferred. In this work, we comparatively analyze two among the most common universal approximators: artificial neural networks (ANN) and radial basis functions (RBF) under different problem settings to estimate their scalability and flexibility in dealing with more and more complex problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower

  15. Study on multi-objective flexible job-shop scheduling problem considering energy consumption

    Directory of Open Access Journals (Sweden)

    Zengqiang Jiang

    2014-06-01

    Full Text Available Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II based on blood variation for above scheduling model.Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model.Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption.Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

  16. Deterministic algorithms for multi-criteria Max-TSP

    NARCIS (Netherlands)

    Manthey, Bodo

    2012-01-01

    We present deterministic approximation algorithms for the multi-criteria maximum traveling salesman problem (Max-TSP). Our algorithms are faster and simpler than the existing randomized algorithms. We devise algorithms for the symmetric and asymmetric multi-criteria Max-TSP that achieve ratios of

  17. Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

    International Nuclear Information System (INIS)

    Vianna Neto, Julio Xavier; Andrade Bernert, Diego Luis de; Santos Coelho, Leandro dos

    2011-01-01

    The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.

  18. Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

    Energy Technology Data Exchange (ETDEWEB)

    Vianna Neto, Julio Xavier, E-mail: julio.neto@onda.com.b [Pontifical Catholic University of Parana, PUCPR, Undergraduate Program at Mechatronics Engineering, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Andrade Bernert, Diego Luis de, E-mail: dbernert@gmail.co [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos Coelho, Leandro dos, E-mail: leandro.coelho@pucpr.b [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil)

    2011-01-15

    The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.

  19. Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

    Energy Technology Data Exchange (ETDEWEB)

    Neto, Julio Xavier Vianna [Pontifical Catholic University of Parana, PUCPR, Undergraduate Program at Mechatronics Engineering, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Bernert, Diego Luis de Andrade; Coelho, Leandro dos Santos [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil)

    2011-01-15

    The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature. (author)

  20. Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2014-01-01

    Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

  1. MultiController: the PLC-SCADA object for advanced regulation

    CERN Document Server

    Ortola, J

    2007-01-01

    Nowadays, industrial solutions with PLCs (Programmable Logic Controllers) have basic control loop features. The SCADA (Supervisory Control And Data Acquisition) system is a key point of the process control system due to an efficient HMI (Human Machine Interfaces) that provides an open method of tuning and leading possibilities. As a consequence, advanced control algorithms have to be developed and implemented for those PLC-SCADA solutions in order to provide perspectives in solving complex and critical regulation problems. The MultiController is an object integrated for a large scale project at CERN (the European Organization for Nuclear Research) named LHC-GCS (Large Hadron Collider - Gas Control System). It is developed for a Framework called CERN-UNICOS based on PLC-SCADA facilities. The MultiController object offers various advanced control loop strategies. It gives to the user advanced control algorithms like PID, Smith Predictor, PFC, GPC and RST. It is implemented as a monolithic entity (in PLC and SCA...

  2. Physical Mapping Using Simulated Annealing and Evolutionary Algorithms

    DEFF Research Database (Denmark)

    Vesterstrøm, Jacob Svaneborg

    2003-01-01

    optimization method when searching for an ordering of the fragments in PM. In this paper, we applied an evolutionary algorithm to the problem, and compared its performance to that of SA and local search on simulated PM data, in order to determine the important factors in finding a good ordering of the segments....... The analysis highlights the importance of a good PM model, a well-correlated fitness function, and high quality hybridization data. We suggest that future work in PM should focus on design of more reliable fitness functions and on developing error-screening algorithms....

  3. Genetic algorithm based separation cascade optimization

    International Nuclear Information System (INIS)

    Mahendra, A.K.; Sanyal, A.; Gouthaman, G.; Bera, T.K.

    2008-01-01

    The conventional separation cascade design procedure does not give an optimum design because of squaring-off, variation of flow rates and separation factor of the element with respect to stage location. Multi-component isotope separation further complicates the design procedure. Cascade design can be stated as a constrained multi-objective optimization. Cascade's expectation from the separating element is multi-objective i.e. overall separation factor, cut, optimum feed and separative power. Decision maker may aspire for more comprehensive multi-objective goals where optimization of cascade is coupled with the exploration of separating element optimization vector space. In real life there are many issues which make it important to understand the decision maker's perception of cost-quality-speed trade-off and consistency of preferences. Genetic algorithm (GA) is one such evolutionary technique that can be used for cascade design optimization. This paper addresses various issues involved in the GA based multi-objective optimization of the separation cascade. Reference point based optimization methodology with GA based Pareto optimality concept for separation cascade was found pragmatic and promising. This method should be explored, tested, examined and further developed for binary as well as multi-component separations. (author)

  4. Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing

    Directory of Open Access Journals (Sweden)

    Kumar Deepak

    2015-12-01

    Full Text Available Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE, Non Dominated Sorting Genetic Algorithm (NSGA II and Simulated Annealing (SA. It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.

  5. Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

    Science.gov (United States)

    Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

    2015-01-01

    The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

  6. Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

    Science.gov (United States)

    Dou, Liyun; Xu, Dan; Chen, Hao; Liu, Yicheng

    2017-07-01

    To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.

  7. Spatial multiobjective optimization of agricultural conservation practices using a SWAT model and an evolutionary algorithm.

    Science.gov (United States)

    Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine

    2012-12-09

    Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a

  8. Multi-objective decision-making framework for effective waste collection in smart cities

    CSIR Research Space (South Africa)

    Manqele, Lindelweyizizwe

    2017-10-01

    Full Text Available T-enabled objects. This implies taking into account multi-objective goals in the collection process while dealing with complexities such as data loss during IoT based data collection. Understanding current decision-making algorithms highlights the deeper insight...

  9. Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic

    Directory of Open Access Journals (Sweden)

    Gannouni Asmae

    2017-01-01

    closed form expression. This novel approach is based on combinatorial probability and can be incorporated in a multiobjective evolutionary algorithm. (iiProvide probabilistic approaches to elitism and diversification in multiobjective evolutionary algorithms. Finally, The behavior of the resulting Probabilistic Multi-objective Evolutionary Algorithms (PrMOEAs is empirically investigated on the multi-objective stochastic VRP problem.

  10. Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

    Directory of Open Access Journals (Sweden)

    Jinjiao Hou

    2018-05-01

    Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

  11. Weighing Efficiency-Robustness in Supply Chain Disruption by Multi-Objective Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Tong Shu

    2016-03-01

    Full Text Available This paper investigates various supply chain disruptions in terms of scenario planning, including node disruption and chain disruption; namely, disruptions in distribution centers and disruptions between manufacturing centers and distribution centers. Meanwhile, it also focuses on the simultaneous disruption on one node or a number of nodes, simultaneous disruption in one chain or a number of chains and the corresponding mathematical models and exemplification in relation to numerous manufacturing centers and diverse products. Robustness of the design of the supply chain network is examined by weighing efficiency against robustness during supply chain disruptions. Efficiency is represented by operating cost; robustness is indicated by the expected disruption cost and the weighing issue is calculated by the multi-objective firefly algorithm for consistency in the results. It has been shown that the total cost achieved by the optimal target function is lower than that at the most effective time of supply chains. In other words, the decrease of expected disruption cost by improving robustness in supply chains is greater than the increase of operating cost by reducing efficiency, thus leading to cost advantage. Consequently, by approximating the Pareto Front Chart of weighing between efficiency and robustness, enterprises can choose appropriate efficiency and robustness for their longer-term development.

  12. Intersection signal control multi-objective optimization based on genetic algorithm

    OpenAIRE

    Zhanhong Zhou; Ming Cai

    2014-01-01

    A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at ...

  13. [Multi-mathematical modelings for compatibility optimization of Jiangzhi granules].

    Science.gov (United States)

    Yang, Ming; Zhang, Li; Ge, Yingli; Lu, Yanliu; Ji, Guang

    2011-12-01

    To investigate into the method of "multi activity index evaluation and combination optimized of mult-component" for Chinese herbal formulas. According to the scheme of uniform experimental design, efficacy experiment, multi index evaluation, least absolute shrinkage, selection operator (LASSO) modeling, evolutionary optimization algorithm, validation experiment, we optimized the combination of Jiangzhi granules based on the activity indexes of blood serum ALT, ALT, AST, TG, TC, HDL, LDL and TG level of liver tissues, ratio of liver tissue to body. Analytic hierarchy process (AHP) combining with criteria importance through intercriteria correlation (CRITIC) for multi activity index evaluation was more reasonable and objective, it reflected the information of activity index's order and objective sample data. LASSO algorithm modeling could accurately reflect the relationship between different combination of Jiangzhi granule and the activity comprehensive indexes. The optimized combination of Jiangzhi granule showed better values of the activity comprehensive indexed than the original formula after the validation experiment. AHP combining with CRITIC can be used for multi activity index evaluation and LASSO algorithm, it is suitable for combination optimized of Chinese herbal formulas.

  14. Multi-objective optimization of cooling air distributions of grate cooler with different clinker particles diameters and air chambers by genetic algorithm

    International Nuclear Information System (INIS)

    Shao, Wei; Cui, Zheng; Cheng, Lin

    2017-01-01

    Highlights: • A multi-objective optimization model of air distributions of grate cooler by genetic algorithm is proposed. • Optimal air distributions of different conditions are obtained and validated by measurements. • The most economic average diameters of clinker particles is 0.02 m. • The most economic amount of air chambers is 9. - Abstract: The paper proposes a multi-objective optimization model of cooling air distributions of grate cooler in cement plant based on convective heat transfer principle and entropy generation minimization analysis. The heat transfer and flow models of clinker cooling process are brought out at first. Then the modified entropy generation numbers caused by heat transfer and viscous dissipation are considered as objective functions respectively which are optimized by genetic algorithm simultaneously. The design variables are superficial velocities of air chambers and thicknesses of clinker layer on different grate plates. The model is verified by a set of Pareto optimal solutions and scattered distributions of design variables. Sensitive analysis of average diameters of clinker particles and amount of air chambers are carried out based on the optimization model. The optimal cooling air distributions are compared by heat recovered, energy consumption of cooling fans and heat efficiency of grate cooler. And all of them are selected from the Pareto optimal solutions based on energy consumption of cooling fans minimization. The results show that the most effective and economic average diameter of clinker particles is 0.02 m and the amount of air chambers is 9.

  15. Genetic algorithm essentials

    CERN Document Server

    Kramer, Oliver

    2017-01-01

    This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

  16. An efficient algorithm for bi-objective combined heat and power production planning under the emission trading scheme

    International Nuclear Information System (INIS)

    Rong, Aiying; Figueira, José Rui; Lahdelma, Risto

    2014-01-01

    Highlights: • Define fuel mix setting for the bi-objective CHP environmental/economic dispatch. • Develop an efficient algorithm for constructing the Pareto frontier for the problem. • Time complexity analysis is conducted for the proposed algorithm. • The algorithm is theoretically compared against a traditional algorithm. • The efficiency of the algorithm is justified by numerical results. - Abstract: The growing environmental awareness and the apparent conflicts between economic and environmental objectives turn energy planning problems naturally into multi-objective optimization problems. In the current study, mixed fuel combustion is considered as an option to achieve tradeoff between economic objective (associated with fuel cost) and emission objective (measured in CO 2 emission cost according to fuels and emission allowance price) because a fuel with higher emissions is usually cheaper than one with lower emissions. Combined heat and power (CHP) production is an important high-efficiency technology to promote under the emission trading scheme. In CHP production, the production planning of both commodities must be done in coordination. A long-term planning problem decomposes into thousands of hourly subproblems. In this paper, a bi-objective multi-period linear programming CHP planning model is presented first. Then, an efficient specialized merging algorithm for constructing the exact Pareto frontier (PF) of the problem is presented. The algorithm is theoretically and empirically compared against a modified dichotomic search algorithm. The efficiency and effectiveness of the algorithm is justified

  17. Multi-objective engineering design using preferences

    Science.gov (United States)

    Sanchis, J.; Martinez, M.; Blasco, X.

    2008-03-01

    System design is a complex task when design parameters have to satisy a number of specifications and objectives which often conflict with those of others. This challenging problem is called multi-objective optimization (MOO). The most common approximation consists in optimizing a single cost index with a weighted sum of objectives. However, once weights are chosen the solution does not guarantee the best compromise among specifications, because there is an infinite number of solutions. A new approach can be stated, based on the designer's experience regarding the required specifications and the associated problems. This valuable information can be translated into preferences for design objectives, and will lead the search process to the best solution in terms of these preferences. This article presents a new method, which enumerates these a priori objective preferences. As a result, a single objective is built automatically and no weight selection need be performed. Problems occuring because of the multimodal nature of the generated single cost index are managed with genetic algorithms (GAs).

  18. Fast stochastic algorithm for simulating evolutionary population dynamics

    Science.gov (United States)

    Tsimring, Lev; Hasty, Jeff; Mather, William

    2012-02-01

    Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

  19. Estimating the ratios of the stationary distribution values for Markov chains modeling evolutionary algorithms.

    Science.gov (United States)

    Mitavskiy, Boris; Cannings, Chris

    2009-01-01

    The evolutionary algorithm stochastic process is well-known to be Markovian. These have been under investigation in much of the theoretical evolutionary computing research. When the mutation rate is positive, the Markov chain modeling of an evolutionary algorithm is irreducible and, therefore, has a unique stationary distribution. Rather little is known about the stationary distribution. In fact, the only quantitative facts established so far tell us that the stationary distributions of Markov chains modeling evolutionary algorithms concentrate on uniform populations (i.e., those populations consisting of a repeated copy of the same individual). At the same time, knowing the stationary distribution may provide some information about the expected time it takes for the algorithm to reach a certain solution, assessment of the biases due to recombination and selection, and is of importance in population genetics to assess what is called a "genetic load" (see the introduction for more details). In the recent joint works of the first author, some bounds have been established on the rates at which the stationary distribution concentrates on the uniform populations. The primary tool used in these papers is the "quotient construction" method. It turns out that the quotient construction method can be exploited to derive much more informative bounds on ratios of the stationary distribution values of various subsets of the state space. In fact, some of the bounds obtained in the current work are expressed in terms of the parameters involved in all the three main stages of an evolutionary algorithm: namely, selection, recombination, and mutation.

  20. CCLab--a multi-objective genetic algorithm based combinatorial library design software and an application for histone deacetylase inhibitor design.

    Science.gov (United States)

    Fang, Guanghua; Xue, Mengzhu; Su, Mingbo; Hu, Dingyu; Li, Yanlian; Xiong, Bing; Ma, Lanping; Meng, Tao; Chen, Yuelei; Li, Jingya; Li, Jia; Shen, Jingkang

    2012-07-15

    The introduction of the multi-objective optimization has dramatically changed the virtual combinatorial library design, which can consider many objectives simultaneously, such as synthesis cost and drug-likeness, thus may increase positive rates of biological active compounds. Here we described a software called CCLab (Combinatorial Chemistry Laboratory) for combinatorial library design based on the multi-objective genetic algorithm. Tests of the convergence ability and the ratio to re-take the building blocks in the reference library were conducted to assess the software in silico, and then it was applied to a real case of designing a 5×6 HDAC inhibitor library. Sixteen compounds in the resulted library were synthesized, and the histone deactetylase (HDAC) enzymatic assays proved that 14 compounds showed inhibitory ratios more than 50% against tested 3 HDAC enzymes at concentration of 20 μg/mL, with IC(50) values of 3 compounds comparable to SAHA. These results demonstrated that the CCLab software could enhance the hit rates of the designed library and would be beneficial for medicinal chemists to design focused library in drug development (the software can be downloaded at: http://202.127.30.184:8080/drugdesign.html). Copyright © 2012 Elsevier Ltd. All rights reserved.

  1. Advances in multi-sensor data fusion: algorithms and applications.

    Science.gov (United States)

    Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

    2009-01-01

    With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

  2. Multi-objective optimization design method of radiation shielding

    International Nuclear Information System (INIS)

    Yang Shouhai; Wang Weijin; Lu Daogang; Chen Yixue

    2012-01-01

    Due to the shielding design goals of diversification and uncertain process of many factors, it is necessary to develop an optimization design method of intelligent shielding by which the shielding scheme selection will be achieved automatically and the uncertainties of human impact will be reduced. For economical feasibility to achieve a radiation shielding design for automation, the multi-objective genetic algorithm optimization of screening code which combines the genetic algorithm and discrete-ordinate method was developed to minimize the costs, size, weight, and so on. This work has some practical significance for gaining the optimization design of shielding. (authors)

  3. Multi-objective optimization of GENIE Earth system models.

    Science.gov (United States)

    Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

    2009-07-13

    The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

  4. A multi-objective genetic approach to domestic load scheduling in an energy management system

    International Nuclear Information System (INIS)

    Soares, Ana; Antunes, Carlos Henggeler; Oliveira, Carlos; Gomes, Álvaro

    2014-01-01

    In this paper a multi-objective genetic algorithm is used to solve a multi-objective model to optimize the time allocation of domestic loads within a planning period of 36 h, in a smart grid context. The management of controllable domestic loads is aimed at minimizing the electricity bill and the end-user’s dissatisfaction concerning two different aspects: the preferred time slots for load operation and the risk of interruption of the energy supply. The genetic algorithm is similar to the Elitist NSGA-II (Nondominated Sorting Genetic Algorithm II), in which some changes have been introduced to adapt it to the physical characteristics of the load scheduling problem and improve usability of results. The mathematical model explicitly considers economical, technical, quality of service and comfort aspects. Illustrative results are presented and the characteristics of different solutions are analyzed. - Highlights: • A genetic algorithm similar to the NSGA-II is used to solve a multi-objective model. • The optimized time allocation of domestic loads in a smart grid context is achieved. • A variable preference profile for the operation of the managed loads is included. • A safety margin is used to account for the quality of the energy services provided. • A non-dominated front with the solutions in the two-objective space is obtained

  5. Prediction and optimization of fuel cell performance using a multi-objective genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Marques Hobold, Gustavo [Laboratory of Energy Conversion Engineering and Technology, Federal University of Santa Catarina (Brazil); Washington University in St. Louis, MO 63130 (United States); Agarwal, Ramesh K. [Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, MO 63130 (United States)

    2013-07-01

    The attention that is currently being given to the emission of pollutant gases in the atmosphere has made the fuel cell (FC), an energy conversion device that cleanly converts chemical energy into electrical energy, a good alternative to other technologies that still use carbon-based fuels. The temperature plays an important role on the efficiency of an FC as it influences directly the humidity of the membrane, the reversible thermodynamic potential and the partial pressure of water; therefore the thermal control of the fuel cell is the focus of this paper. We present models for both high and low temperature fuel cells based on the solid-oxide fuel cell (SOFC) and the polymer electrolyte membrane fuel cell (PEMFC). A thermodynamic analysis is performed on the cells and the methods of controlling their temperature are discussed. The cell parameters are optimized for both high and low temperatures using a Java-based multi-objective genetic algorithm, which makes use of the logic of the biological theory of evolution to classify individual parameters based on a fitness function in order to maximize the power of the fuel cell. Applications to high and low temperature fuel cells are discussed.

  6. Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutations

    DEFF Research Database (Denmark)

    Kratsch, Stefan; Lehre, Per Kristian; Neumann, Frank

    2011-01-01

    Evolutionary algorithms have been shown to be very successful for a wide range of NP-hard combinatorial optimization problems. We investigate the NP-hard problem of computing a spanning tree that has a maximal number of leaves by evolutionary algorithms in the context of fixed parameter tractabil...... two common mutation operators, we show that an operator related to spanning tree problems leads to an FPT running time in contrast to a general mutation operator that does not have this property....

  7. Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

    Science.gov (United States)

    Joseph, R.; Courbin, F.; Starck, J.-L.

    2016-05-01

    We introduce a new algorithm for colour separation and deblending of multi-band astronomical images called MuSCADeT which is based on Morpho-spectral Component Analysis of multi-band images. The MuSCADeT algorithm takes advantage of the sparsity of astronomical objects in morphological dictionaries such as wavelets and their differences in spectral energy distribution (SED) across multi-band observations. This allows us to devise a model independent and automated approach to separate objects with different colours. We show with simulations that we are able to separate highly blended objects and that our algorithm is robust against SED variations of objects across the field of view. To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies. Although the main driver for our work is the deblending of strong gravitational lenses, our method is fit to be used for any purpose related to deblending of objects in astronomical images. An example of such an application is the separation of the red and blue stellar populations of a spiral galaxy in the galaxy cluster Abell 2744. We provide a python package along with all simulations and routines used in this paper to contribute to reproducible research efforts. Codes can be found at http://lastro.epfl.ch/page-126973.html

  8. A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Sho Fukuda

    2014-12-01

    Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

  9. Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms.

    Science.gov (United States)

    Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam

    2016-03-01

    A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.

  10. An Evolutionary Approach for Robust Layout Synthesis of MEMS

    DEFF Research Database (Denmark)

    Fan, Zhun; Wang, Jiachuan; Goodman, Erik

    2005-01-01

    The paper introduces a robust design method for layout synthesis of MEM resonators subject to inherent geometric uncertainties such as the fabrication error on the sidewall of the structure. The robust design problem is formulated as a multi-objective constrained optimisation problem after certain...... assumptions and treated with multiobjective genetic algorithm (MOGA), a special type of evolutionary computing approaches. Case study based on layout synthesis of a comb-driven MEM resonator shows that the approach proposed in this paper can lead to design results that meet the target performance and are less...

  11. Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

    Directory of Open Access Journals (Sweden)

    Xiaoya Ma

    2015-11-01

    Full Text Available As the main feature of land use planning, land use allocation (LUA optimization is an important means of creating a balance between the land-use supply and demand in a region and promoting the sustainable utilization of land resources. In essence, LUA optimization is a multi-objective optimization problem under the land use supply and demand constraints in a region. In order to obtain a better sustainable multi-objective LUA optimization solution, the present study proposes a LUA model based on the multi-objective artificial immune optimization algorithm (MOAIM-LUA model. The main achievements of the present study are as follows: (a the land-use supply and demand factors are analyzed and the constraint conditions of LUA optimization problems are constructed based on the analysis framework of the balance between the land use supply and demand; (b the optimization objectives of LUA optimization problems are defined and modeled using ecosystem service value theory and land rent and price theory; and (c a multi-objective optimization algorithm is designed for solving multi-objective LUA optimization problems based on the novel immune clonal algorithm (NICA. On the basis of the aforementioned achievements, MOAIM-LUA was applied to a real case study of land-use planning in Anlu County, China. Compared to the current land use situation in Anlu County, optimized LUA solutions offer improvements in the social and ecological objective areas. Compared to the existing models, such as the non-dominated sorting genetic algorithm-II, experimental results demonstrate that the model designed in the present study can obtain better non-dominated solution sets and is superior in terms of algorithm stability.

  12. The multi-niche crowding genetic algorithm: Analysis and applications

    Energy Technology Data Exchange (ETDEWEB)

    Cedeno, Walter [Univ. of California, Davis, CA (United States)

    1995-09-01

    The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.

  13. Evolutionary Algorithms For Neural Networks Binary And Real Data Classification

    Directory of Open Access Journals (Sweden)

    Dr. Hanan A.R. Akkar

    2015-08-01

    Full Text Available Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in prediction clustering classification and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as Iris and Ecoli.

  14. Analog Group Delay Equalizers Design Based on Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    M. Laipert

    2006-04-01

    Full Text Available This paper deals with a design method of the analog all-pass filter designated for equalization of the group delay frequency response of the analog filter. This method is based on usage of evolutionary algorithm, the Differential Evolution algorithm in particular. We are able to design such equalizers to be obtained equal-ripple group delay frequency response in the pass-band of the low-pass filter. The procedure works automatically without an input estimation. The method is presented on solving practical examples.

  15. Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm

    International Nuclear Information System (INIS)

    Yang, Yu; Fritzsching, Keith J.; Hong, Mei

    2013-01-01

    A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra (“good connections”), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra (“bad connections”), and minimizing the number of assigned peaks that have no matching peaks in the other spectra (“edges”). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct

  16. Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm.

    Science.gov (United States)

    Yang, Yu; Fritzsching, Keith J; Hong, Mei

    2013-11-01

    A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra ("good connections"), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra ("bad connections"), and minimizing the number of assigned peaks that have no matching peaks in the other spectra ("edges"). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a

  17. Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation

    International Nuclear Information System (INIS)

    Tian, Hao; Yuan, Xiaohui; Ji, Bin; Chen, Zhihuan

    2014-01-01

    Highlights: • An improved non-dominated sorting gravitational search algorithm (NSGSA-CM) is proposed. • NSGSA-CM is used to solve the problem of short-term multi-objective hydrothermal scheduling. • We enhance the search capability of NSGSA-CM by chaotic mutation. • New strategies are devised to handle various constraints in NSGSA-CM. • We obtain better compromise solutions with less fuel cost and emissions. - Abstract: This paper proposes a non-dominated sorting gravitational search algorithm with chaotic mutation (NSGSA-CM) to solve short-term economic/environmental hydrothermal scheduling (SEEHTS) problem. The SEEHTS problem is formulated as a multi-objective optimization problem with many equality and inequality constraints. By introducing the concept of non-dominated sorting and crowding distance, NSGSA-CM can optimize two objectives of fuel cost and pollutant emission simultaneously and obtain a set of Pareto optimal solutions in one trial. In order to improve the performance of NSGSA-CM, the paper introduces particle memory character and population social information in velocity update process. And a chaotic mutation is adopted to prevent the premature convergence. Furthermore, NSGSA-CM utilizes an elitism strategy which selects better solutions in parent and offspring populations based on their non-domination rank and crowding distance to update new generations. When dealing with the constraints of the SEEHTS, new strategies without penalty factors are proposed. In order to handle the water dynamic balance and system load balance constraints, this paper uses a combined strategy which adjusts the violation averagely to each decision variable at first and adjusts the rest violation randomly later. Meanwhile, a new symmetrical adjustment strategy by modifying the discharges at current and later interval without breaking water dynamic balance is adopted to handle reservoir storage constraints. To test the performance of the proposed NSGSA

  18. Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

    Energy Technology Data Exchange (ETDEWEB)

    Ariyarit, Atthaphon; Kanazaki, Masahiro [Tokyo Metropolitan University, Tokyo (Japan)

    2015-04-15

    This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

  19. Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

    International Nuclear Information System (INIS)

    Ariyarit, Atthaphon; Kanazaki, Masahiro

    2015-01-01

    This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

  20. A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

    Directory of Open Access Journals (Sweden)

    J. S. Sadaghiani

    2014-04-01

    Full Text Available Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions.

  1. A multi-objective location routing problem using imperialist competitive algorithm

    Directory of Open Access Journals (Sweden)

    Amir Mohammad Golmohammadi

    2016-06-01

    Full Text Available Nowadays, most manufacturing units try to locate their requirements and the depot vehicle routing in order to transport the goods at optimum cost. Needless to mention that the locations of the required warehouses influence on the performance of vehicle routing. In this paper, a mathematical programming model to optimize the storage location and vehicle routing are presented. The first objective function of the model minimizes the total cost associated with the transportation and storage, and the second objective function minimizes the difference distance traveled by vehicles. The study uses Imperialist Competitive Algorithm (ICA to solve the resulted problems in different sizes. The preliminary results have indicated that the proposed study has performed better than NSGA-II and PAES methods in terms of Quality metric and Spacing metric.

  2. Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

    Science.gov (United States)

    Sahraei, S.; Asadzadeh, M.

    2017-12-01

    Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

  3. Constrained multi-objective optimization of storage ring lattices

    Science.gov (United States)

    Husain, Riyasat; Ghodke, A. D.

    2018-03-01

    The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

  4. Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic

    DEFF Research Database (Denmark)

    Govindan, Kannan; Jafarian, Ahmad; Nourbakhsh, Vahid

    2015-01-01

    simultaneously considering the sustainable OAP in the sustainable SCND as a strategic decision. The proposed supply chain network is composed of five echelons including suppliers classified in different classes, plants, distribution centers that dispatch products via two different ways, direct shipment......, a novel multi-objective hybrid approach called MOHEV with two strategies for its best particle selection procedure (BPSP), minimum distance, and crowding distance is proposed. MOHEV is constructed through hybridization of two multi-objective algorithms, namely the adapted multi-objective electromagnetism...

  5. Study on the evolutionary optimisation of the topology of network control systems

    Science.gov (United States)

    Zhou, Zude; Chen, Benyuan; Wang, Hong; Fan, Zhun

    2010-08-01

    Computer networks have been very popular in enterprise applications. However, optimisation of network designs that allows networks to be used more efficiently in industrial environment and enterprise applications remains an interesting research topic. This article mainly discusses the topology optimisation theory and methods of the network control system based on switched Ethernet in an industrial context. Factors that affect the real-time performance of the industrial control network are presented in detail, and optimisation criteria with their internal relations are analysed. After the definition of performance parameters, the normalised indices for the evaluation of the topology optimisation are proposed. The topology optimisation problem is formulated as a multi-objective optimisation problem and the evolutionary algorithm is applied to solve it. Special communication characteristics of the industrial control network are considered in the optimisation process. In respect to the evolutionary algorithm design, an improved arena algorithm is proposed for the construction of the non-dominated set of the population. In addition, for the evaluation of individuals, the integrated use of the dominative relation method and the objective function combination method, for reducing the computational cost of the algorithm, are given. Simulation tests show that the performance of the proposed algorithm is preferable and superior compared to other algorithms. The final solution greatly improves the following indices: traffic localisation, traffic balance and utilisation rate balance of switches. In addition, a new performance index with its estimation process is proposed.

  6. Multi-objective genetic algorithm parameter estimation in a reduced nuclear reactor model

    Energy Technology Data Exchange (ETDEWEB)

    Marseguerra, M.; Zio, E.; Canetta, R. [Polytechnic of Milan, Dept. of Nuclear Engineering, Milano (Italy)

    2005-07-01

    The fast increase in computing power has rendered, and will continue to render, more and more feasible the incorporation of dynamics in the safety and reliability models of complex engineering systems. In particular, the Monte Carlo simulation framework offers a natural environment for estimating the reliability of systems with dynamic features. However, the time-integration of the dynamic processes may render the Monte Carlo simulation quite burdensome so that it becomes mandatory to resort to validated, simplified models of process evolution. Such models are typically based on lumped effective parameters whose values need to be suitably estimated so as to best fit to the available plant data. In this paper we propose a multi-objective genetic algorithm approach for the estimation of the effective parameters of a simplified model of nuclear reactor dynamics. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest to the actual evolution profiles. A case study is reported in which the real reactor is simulated by the QUAndry based Reactor Kinetics (Quark) code available from the Nuclear Energy Agency and the simplified model is based on the point kinetics approximation to describe the neutron balance in the core and on thermal equilibrium relations to describe the energy exchange between the different loops. (authors)

  7. Multi-objective genetic algorithm parameter estimation in a reduced nuclear reactor model

    International Nuclear Information System (INIS)

    Marseguerra, M.; Zio, E.; Canetta, R.

    2005-01-01

    The fast increase in computing power has rendered, and will continue to render, more and more feasible the incorporation of dynamics in the safety and reliability models of complex engineering systems. In particular, the Monte Carlo simulation framework offers a natural environment for estimating the reliability of systems with dynamic features. However, the time-integration of the dynamic processes may render the Monte Carlo simulation quite burdensome so that it becomes mandatory to resort to validated, simplified models of process evolution. Such models are typically based on lumped effective parameters whose values need to be suitably estimated so as to best fit to the available plant data. In this paper we propose a multi-objective genetic algorithm approach for the estimation of the effective parameters of a simplified model of nuclear reactor dynamics. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest to the actual evolution profiles. A case study is reported in which the real reactor is simulated by the QUAndry based Reactor Kinetics (Quark) code available from the Nuclear Energy Agency and the simplified model is based on the point kinetics approximation to describe the neutron balance in the core and on thermal equilibrium relations to describe the energy exchange between the different loops. (authors)

  8. Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

    International Nuclear Information System (INIS)

    Zio, E.; Golea, L.R.; Rocco S, C.M.

    2012-01-01

    In this paper, an analysis of the vulnerability of the Italian high-voltage (380 kV) electrical transmission network (HVIET) is carried out for the identification of the groups of links (or edges, or arcs) most critical considering the network structure and flow. Betweenness centrality and network connection efficiency variations are considered as measures of the importance of the network links. The search of the most critical ones is carried out within a multi-objective optimization problem aimed at the maximization of the importance of the groups and minimization of their dimension. The problem is solved using a genetic algorithm. The analysis is based only on information on the topology of the network and leads to the identification of the most important single component, couples of components, triplets and so forth. The comparison of the results obtained with those reported by previous analyses indicates that the proposed approach provides useful complementary information.

  9. Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

    Science.gov (United States)

    2009-03-01

    A. Van Veldhuizen . Evolutionary Algorithms for Solving Multi-Objective Problems, chapter MOEA Parallelization. Springer, 2007. 37. Das, Subrata...Mike P. and Willem-Jan van den Heuvel. Service Oriented Architectures: Approaches, Technologies, and Research Issues. Technical report, Tilburg...Tanenbaum, Andrew S. and Maarten Van Steen. Distributed Systems: Principles and Paradigms. Prentice Hall, 2006. 128. Thomas, Tavaris J. Fire Ant: An

  10. Attribute Index and Uniform Design Based Multiobjective Association Rule Mining with Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    2013-01-01

    Full Text Available In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

  11. Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm.

    Science.gov (United States)

    Zhang, Jie; Wang, Yuping; Feng, Junhong

    2013-01-01

    In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

  12. Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

    Science.gov (United States)

    Yu, Gwo-Ruey; Huang, Yu-Chia; Cheng, Chih-Yung

    2016-07-01

    In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.

  13. Water distribution systems design optimisation using metaheuristics ...

    African Journals Online (AJOL)

    The topic of multi-objective water distribution systems (WDS) design optimisation using metaheuristics is investigated, comparing numerous modern metaheuristics, including several multi-objective evolutionary algorithms, an estimation of distribution algorithm and a recent hyperheuristic named AMALGAM (an evolutionary ...

  14. Evolutionary algorithm for optimization of nonimaging Fresnel lens geometry.

    Science.gov (United States)

    Yamada, N; Nishikawa, T

    2010-06-21

    In this study, an evolutionary algorithm (EA), which consists of genetic and immune algorithms, is introduced to design the optical geometry of a nonimaging Fresnel lens; this lens generates the uniform flux concentration required for a photovoltaic cell. Herein, a design procedure that incorporates a ray-tracing technique in the EA is described, and the validity of the design is demonstrated. The results show that the EA automatically generated a unique geometry of the Fresnel lens; the use of this geometry resulted in better uniform flux concentration with high optical efficiency.

  15. Performance improvement of an active vibration absorber subsystem for an aircraft model using a bees algorithm based on multi-objective intelligent optimization

    Science.gov (United States)

    Zarchi, Milad; Attaran, Behrooz

    2017-11-01

    This study develops a mathematical model to investigate the behaviour of adaptable shock absorber dynamics for the six-degree-of-freedom aircraft model in the taxiing phase. The purpose of this research is to design a proportional-integral-derivative technique for control of an active vibration absorber system using a hydraulic nonlinear actuator based on the bees algorithm. This optimization algorithm is inspired by the natural intelligent foraging behaviour of honey bees. The neighbourhood search strategy is used to find better solutions around the previous one. The parameters of the controller are adjusted by minimizing the aircraft's acceleration and impact force as the multi-objective function. The major advantages of this algorithm over other optimization algorithms are its simplicity, flexibility and robustness. The results of the numerical simulation indicate that the active suspension increases the comfort of the ride for passengers and the fatigue life of the structure. This is achieved by decreasing the impact force, displacement and acceleration significantly.

  16. Improving the Penetration of Wind Power with Dynamic Thermal Rating System, Static VAR Compensator and Multi-Objective Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Jiashen Teh

    2018-04-01

    Full Text Available The integration of renewable energy sources, especially wind energy, has been on the rise throughout power systems worldwide. Due to this relatively new introduction, the integration of wind energy is often not optimized. Moreover, owing to the technical constraints and transmission congestions of the power network, most of the wind energy has to be curtailed. Due to various factors that influence the connectivity of wind energy, this paper proposes a well-organized posterior multi-objective (MO optimization algorithm for maximizing the connections of wind energy. In this regard, the dynamic thermal rating (DTR system and the static VAR compensator (SVC have been identified as effective tools for improving the loadability of the network. The propose MO algorithm in this paper aims to minimize: (1 wind energy curtailment, (2 operation cost of the network considering all investments and operations, also known as the total social cost, and (3 SVC operation cost. The proposed MO problem was solved using the non-dominated sorting genetic algorithm (NSGA II and it was tested on the modified IEEE reliability test system (IEEE-RTS. The results demonstrate the applicability of the proposed algorithm in aiding power system enhancement planning for integrating wind energy.

  17. Harmonic elimination in diode-clamped multilevel inverter using evolutionary algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Barkati, Said [Laboratoire d' analyse des Signaux et Systemes (LASS), Universite de M' sila, BP. 166, rue Ichbilia 28000 M' sila (Algeria); Baghli, Lotfi [Groupe de Recherche en Electrotechnique et Electronique de Nancy (GREEN), CNRS UMR 7030, Universite Henri Poincare Nancy 1, BP. 239, 54506 Vandoeuvre-les-Nancy (France); Berkouk, El Madjid; Boucherit, Mohamed-Seghir [Laboratoire de Commande des Processus (LCP), Ecole Nationale Polytechnique, BP. 182, 10 Avenue Hassen Badi, 16200 El Harrach, Alger (Algeria)

    2008-10-15

    This paper describes two evolutionary algorithms for the optimized harmonic stepped-waveform technique. Genetic algorithms and particle swarm optimization are applied to compute the switching angles in a three-phase seven-level inverter to produce the required fundamental voltage while, at the same time, specified harmonics are eliminated. Furthermore, these algorithms are also used to solve the starting point problem of the Newton-Raphson conventional method. This combination provides a very effective method for the harmonic elimination technique. This strategy is useful for different structures of seven-level inverters. The diode-clamped topology is considered in this study. (author)

  18. General upper bounds on the runtime of parallel evolutionary algorithms.

    Science.gov (United States)

    Lässig, Jörg; Sudholt, Dirk

    2014-01-01

    We present a general method for analyzing the runtime of parallel evolutionary algorithms with spatially structured populations. Based on the fitness-level method, it yields upper bounds on the expected parallel runtime. This allows for a rigorous estimate of the speedup gained by parallelization. Tailored results are given for common migration topologies: ring graphs, torus graphs, hypercubes, and the complete graph. Example applications for pseudo-Boolean optimization show that our method is easy to apply and that it gives powerful results. In our examples the performance guarantees improve with the density of the topology. Surprisingly, even sparse topologies such as ring graphs lead to a significant speedup for many functions while not increasing the total number of function evaluations by more than a constant factor. We also identify which number of processors lead to the best guaranteed speedups, thus giving hints on how to parameterize parallel evolutionary algorithms.

  19. Comparison of evolutionary algorithms in gene regulatory network model inference.

    LENUS (Irish Health Repository)

    2010-01-01

    ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

  20. A multi-objective approach to the assignment of stock keeping units to unidirectional picking lines

    Directory of Open Access Journals (Sweden)

    Le Roux, G. J.

    2017-05-01

    Full Text Available An order picking system in a distribution centre consisting of parallel unidirectional picking lines is considered. The objectives are to minimise the walking distance of the pickers, the largest volume of stock on a picking line over all picking lines, the number of small packages, and the total penalty incurred for late distributions. The problem is formulated as a multi-objective multiple knapsack problem that is not solvable in a realistic time. Population-based algorithms, including the artificial bee colony algorithm and the genetic algorithm, are also implemented. The results obtained from all algorithms indicate a substantial improvement on all objectives relative to historical assignments. The genetic algorithm delivers the best performance.

  1. Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

    Science.gov (United States)

    Omagari, Hiroki; Higashino, Shin-Ichiro

    2018-04-01

    In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

  2. Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

    Science.gov (United States)

    Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

    2014-01-16

    To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high

  3. A kNN method that uses a non-natural evolutionary algorithm for ...

    African Journals Online (AJOL)

    We used this algorithm for component selection of a kNN (k Nearest Neighbor) method for breast cancer prognosis. Results with the UCI prognosis data set show that we can find components that help improve the accuracy of kNN by almost 3%, raising it above 79%. Keywords: kNN; classification; evolutionary algorithm; ...

  4. Parameterless evolutionary algorithm applied to the nuclear reload problem

    International Nuclear Information System (INIS)

    Caldas, Gustavo Henrique Flores; Schirru, Roberto

    2008-01-01

    In this work, an evolutionary algorithm with no parameters called FPBIL (parameter free PBIL) is developed based on PBIL (population-based incremental learning). Moreover, the analysis reveals how the parameters from PBIL can be replaced by self-adaptable mechanisms which appear from the radically different form by which the evolution is processed. Despite the advantages, the FPBIL reveals itself compact and relatively modest in the use of computational resources. The FPBIL is then applied to the nuclear reload problem. The experimental results observed are compared to those of other works and corroborate to affirm the superiority of the new algorithm

  5. CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

    Science.gov (United States)

    Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

    2017-07-01

    Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

  6. A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Shan Li

    2014-01-01

    Full Text Available With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

  7. Algorithmic foundation of multi-scale spatial representation

    CERN Document Server

    Li, Zhilin

    2006-01-01

    With the widespread use of GIS, multi-scale representation has become an important issue in the realm of spatial data handling. However, no book to date has systematically tackled the different aspects of this discipline. Emphasizing map generalization, Algorithmic Foundation of Multi-Scale Spatial Representation addresses the mathematical basis of multi-scale representation, specifically, the algorithmic foundation.Using easy-to-understand language, the author focuses on geometric transformations, with each chapter surveying a particular spatial feature. After an introduction to the essential operations required for geometric transformations as well as some mathematical and theoretical background, the book describes algorithms for a class of point features/clusters. It then examines algorithms for individual line features, such as the reduction of data points, smoothing (filtering), and scale-driven generalization, followed by a discussion of algorithms for a class of line features including contours, hydrog...

  8. Robust multi-objective calibration strategies – possibilities for improving flood forecasting

    Directory of Open Access Journals (Sweden)

    G. H. Schmitz

    2012-10-01

    Full Text Available Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008 studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE. This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify

  9. PARETO OPTIMAL SOLUTIONS FOR MULTI-OBJECTIVE GENERALIZED ASSIGNMENT PROBLEM

    Directory of Open Access Journals (Sweden)

    S. Prakash

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: The Multi-Objective Generalized Assignment Problem (MGAP with two objectives, where one objective is linear and the other one is non-linear, has been considered, with the constraints that a job is assigned to only one worker – though he may be assigned more than one job, depending upon the time available to him. An algorithm is proposed to find the set of Pareto optimal solutions of the problem, determining assignments of jobs to workers with two objectives without setting priorities for them. The two objectives are to minimise the total cost of the assignment and to reduce the time taken to complete all the jobs.

    AFRIKAANSE OPSOMMING: ‘n Multi-doelwit veralgemeende toekenningsprobleem (“multi-objective generalised assignment problem – MGAP” met twee doelwitte, waar die een lineêr en die ander nielineêr is nie, word bestudeer, met die randvoorwaarde dat ‘n taak slegs toegedeel word aan een werker – alhoewel meer as een taak aan hom toegedeel kan word sou die tyd beskikbaar wees. ‘n Algoritme word voorgestel om die stel Pareto-optimale oplossings te vind wat die taaktoedelings aan werkers onderhewig aan die twee doelwitte doen sonder dat prioriteite toegeken word. Die twee doelwitte is om die totale koste van die opdrag te minimiseer en om die tyd te verminder om al die take te voltooi.

  10. Many-objective robust decision making for water allocation under climate change.

    Science.gov (United States)

    Yan, Dan; Ludwig, Fulco; Huang, He Qing; Werners, Saskia E

    2017-12-31

    Water allocation is facing profound challenges due to climate change uncertainties. To identify adaptive water allocation strategies that are robust to climate change uncertainties, a model framework combining many-objective robust decision making and biophysical modeling is developed for large rivers. The framework was applied to the Pearl River basin (PRB), China where sufficient flow to the delta is required to reduce saltwater intrusion in the dry season. Before identifying and assessing robust water allocation plans for the future, the performance of ten state-of-the-art MOEAs (multi-objective evolutionary algorithms) is evaluated for the water allocation problem in the PRB. The Borg multi-objective evolutionary algorithm (Borg MOEA), which is a self-adaptive optimization algorithm, has the best performance during the historical periods. Therefore it is selected to generate new water allocation plans for the future (2079-2099). This study shows that robust decision making using carefully selected MOEAs can help limit saltwater intrusion in the Pearl River Delta. However, the framework could perform poorly due to larger than expected climate change impacts on water availability. Results also show that subjective design choices from the researchers and/or water managers could potentially affect the ability of the model framework, and cause the most robust water allocation plans to fail under future climate change. Developing robust allocation plans in a river basin suffering from increasing water shortage requires the researchers and water managers to well characterize future climate change of the study regions and vulnerabilities of their tools. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Patel G.C.M.

    2016-09-01

    Full Text Available The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.. It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA, particle swarm optimization (PSO and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.

  12. Identification of Water Diffusivity of Inorganic Porous Materials Using Evolutionary Algorithms

    Czech Academy of Sciences Publication Activity Database

    Kočí, J.; Maděra, J.; Jerman, M.; Keppert, M.; Svora, Petr; Černý, R.

    2016-01-01

    Roč. 113, č. 1 (2016), s. 51-66 ISSN 0169-3913 Institutional support: RVO:61388980 Keywords : Evolutionary algorithms * Water transport * Inorganic porous materials * Inverse analysis Subject RIV: CA - Inorganic Chemistry Impact factor: 2.205, year: 2016

  13. Comparison of some evolutionary algorithms for optimization of the path synthesis problem

    Science.gov (United States)

    Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna

    2018-01-01

    The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.

  14. The mixing evolutionary algorithm : indepedent selection and allocation of trials

    NARCIS (Netherlands)

    C.H.M. van Kemenade

    1997-01-01

    textabstractWhen using an evolutionary algorithm to solve a problem involving building blocks we have to grow the building blocks and then mix these building blocks to obtain the (optimal) solution. Finding a good balance between the growing and the mixing process is a prerequisite to get a reliable

  15. Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties

    International Nuclear Information System (INIS)

    Wang, Bo; Wang, Shuming; Zhou, Xianzhong; Watada, Junzo

    2016-01-01

    Recent years have witnessed the ever increasing renewable penetration in power generation systems, which entails modern unit commitment problems with modelling and computation burdens. This study aims to simulate the impacts of manifold uncertainties on system operation with emission concerns. First, probability theory and fuzzy set theory are applied to jointly represent the uncertainties such as wind generation, load fluctuation and unit outage that interleaved in unit commitment problems. Second, a Value-at-Risk-based multi-objective approach is developed as a bridge of existing stochastic and robust unit commitment optimizations, which not only captures the inherent conflict between operation cost and supply reliability, but also provides easy-to-adjust robustness against worst-case scenarios. Third, a multi-objective algorithm that integrates fuzzy simulation and particle swarm optimization is developed to achieve approximate Pareto-optimal solutions. The research effectiveness is exemplified by two case studies: The comparison between test systems with and without generation uncertainty demonstrates that this study is practicable and can suggest operational insights of generation mix systems. The sensitivity analysis on Value-at-Risk proves that our method can achieve adequate tradeoff between performance optimality and robustness, thus help system operators in making informed decisions. Finally, the model and algorithm comparisons also justify the superiority of this research. - Highlights: • Probability theory and fuzzy set theory are used to describe different uncertainties. • A Value-at-Risk-based multi-objective unit commitment model is proposed. • An improved multi-objective particle swarm optimization algorithm is developed. • The model achieves adequate trade-off between performance optimality and robustness. • The algorithm can obtain convergent and diversified Pareto fronts.

  16. DNA evolutionary algorithm (DNAEA) for source term identification in convection-diffusion equation

    International Nuclear Information System (INIS)

    Yang, X-H; Hu, X-X; Shen, Z-Y

    2008-01-01

    The source identification problem is changed into an optimization problem in this paper. This is a complicated nonlinear optimization problem. It is very intractable with traditional optimization methods. So DNA evolutionary algorithm (DNAEA) is presented to solve the discussed problem. In this algorithm, an initial population is generated by a chaos algorithm. With the shrinking of searching range, DNAEA gradually directs to an optimal result with excellent individuals obtained by DNAEA. The position and intensity of pollution source are well found with DNAEA. Compared with Gray-coded genetic algorithm and pure random search algorithm, DNAEA has rapider convergent speed and higher calculation precision

  17. Pareto-Optimal Multi-objective Inversion of Geophysical Data

    Science.gov (United States)

    Schnaidt, Sebastian; Conway, Dennis; Krieger, Lars; Heinson, Graham

    2018-01-01

    In the process of modelling geophysical properties, jointly inverting different data sets can greatly improve model results, provided that the data sets are compatible, i.e., sensitive to similar features. Such a joint inversion requires a relationship between the different data sets, which can either be analytic or structural. Classically, the joint problem is expressed as a scalar objective function that combines the misfit functions of multiple data sets and a joint term which accounts for the assumed connection between the data sets. This approach suffers from two major disadvantages: first, it can be difficult to assess the compatibility of the data sets and second, the aggregation of misfit terms introduces a weighting of the data sets. We present a pareto-optimal multi-objective joint inversion approach based on an existing genetic algorithm. The algorithm treats each data set as a separate objective, avoiding forced weighting and generating curves of the trade-off between the different objectives. These curves are analysed by their shape and evolution to evaluate data set compatibility. Furthermore, the statistical analysis of the generated solution population provides valuable estimates of model uncertainty.

  18. Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods

    Directory of Open Access Journals (Sweden)

    B. Y. Qu

    2017-01-01

    Full Text Available Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.

  19. Optimal design and management of chlorination in drinking water networks: a multi-objective approach using Genetic Algorithms and the Pareto optimality concept

    Science.gov (United States)

    Nouiri, Issam

    2017-11-01

    This paper presents the development of multi-objective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objectives have been considered: the improvement of the chlorination uniformity (healthy objective), the minimization of chlorine booster stations number, and the injected chlorine mass (economic objectives). The problem has been dissociated in medium and short terms ones. The proposed methodology was tested on hypothetical and real DWN. Results proved the ability of the developed optimization tool to identify relationships between the healthy and economic objectives as Pareto fronts. The proposed approach was efficient in computing solutions ensuring better chlorination uniformity while requiring the weakest injected chlorine mass when compared to other approaches. For the real DWN studied, chlorination optimization has been crowned by great improvement of free-chlorine-dosing uniformity and by a meaningful chlorine mass reduction, in comparison with the conventional chlorination.

  20. A Hybrid Quantum Evolutionary Algorithm with Improved Decoding Scheme for a Robotic Flow Shop Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Weidong Lei

    2017-01-01

    Full Text Available We aim at solving the cyclic scheduling problem with a single robot and flexible processing times in a robotic flow shop, which is a well-known optimization problem in advanced manufacturing systems. The objective of the problem is to find an optimal robot move sequence such that the throughput rate is maximized. We propose a hybrid algorithm based on the Quantum-Inspired Evolutionary Algorithm (QEA and genetic operators for solving the problem. The algorithm integrates three different decoding strategies to convert quantum individuals into robot move sequences. The Q-gate is applied to update the states of Q-bits in each individual. Besides, crossover and mutation operators with adaptive probabilities are used to increase the population diversity. A repairing procedure is proposed to deal with infeasible individuals. Comparison results on both benchmark and randomly generated instances demonstrate that the proposed algorithm is more effective in solving the studied problem in terms of solution quality and computational time.

  1. Evolutionary algorithms for the Vehicle Routing Problem with Time Windows

    NARCIS (Netherlands)

    Bräysy, Olli; Dullaert, Wout; Gendreau, Michel

    2004-01-01

    This paper surveys the research on evolutionary algorithms for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from a single depot to a set of geographically scattered points. The routes must be designed in such a way

  2. Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

    OpenAIRE

    Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

    2015-01-01

    Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

  3. Multi-objective Design Method for Hybrid Active Power Filter

    Science.gov (United States)

    Yu, Jingrong; Deng, Limin; Liu, Maoyun; Qiu, Zhifeng

    2017-10-01

    In this paper, a multi-objective optimal design for transformerless hybrid active power filter (HAPF) is proposed. The interactions between the active and passive circuits is analyzed, and by taking the interactions into consideration, a three-dimensional objective problem comprising of performance, efficiency and cost of HAPF system is formulated. To deal with the multiple constraints and the strong coupling characteristics of the optimization model, a novel constraint processing mechanism based on distance measurement and adaptive penalty function is presented. In order to improve the diversity of optimal solution and the local searching ability of the particle swarm optimization (PSO) algorithm, a chaotic mutation operator based on multistage neighborhood is proposed. The simulation results show that the optimums near the ordinate origin of the three-dimension space make better tradeoff among the performance, efficiency and cost of HAPF, and the experimental results of transformerless HAPF verify the effectiveness of the method for multi-objective optimization and design.

  4. A Novel Evolutionary Algorithm Inspired by Beans Dispersal

    Directory of Open Access Journals (Sweden)

    Xiaoming Zhang

    2013-02-01

    Full Text Available Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wetsarsquo; research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications.

  5. A multi-objective optimization problem for multi-state series-parallel systems: A two-stage flow-shop manufacturing system

    International Nuclear Information System (INIS)

    Azadeh, A.; Maleki Shoja, B.; Ghanei, S.; Sheikhalishahi, M.

    2015-01-01

    This research investigates a redundancy-scheduling optimization problem for a multi-state series parallel system. The system is a flow shop manufacturing system with multi-state machines. Each manufacturing machine may have different performance rates including perfect performance, decreased performance and complete failure. Moreover, warm standby redundancy is considered for the redundancy allocation problem. Three objectives are considered for the problem: (1) minimizing system purchasing cost, (2) minimizing makespan, and (3) maximizing system reliability. Universal generating function is employed to evaluate system performance and overall reliability of the system. Since the problem is in the NP-hard class of combinatorial problems, genetic algorithm (GA) is used to find optimal/near optimal solutions. Different test problems are generated to evaluate the effectiveness and efficiency of proposed approach and compared to simulated annealing optimization method. The results show the proposed approach is capable of finding optimal/near optimal solution within a very reasonable time. - Highlights: • A redundancy-scheduling optimization problem for a multi-state series parallel system. • A flow shop with multi-state machines and warm standby redundancy. • Objectives are to optimize system purchasing cost, makespan and reliability. • Different test problems are generated and evaluated by a unique genetic algorithm. • It locates optimal/near optimal solution within a very reasonable time

  6. Multi objective multi refinery optimization with environmental and catastrophic failure effects objectives

    Science.gov (United States)

    Khogeer, Ahmed Sirag

    2005-11-01

    Petroleum refining is a capital-intensive business. With stringent environmental regulations on the processing industry and declining refining margins, political instability, increased risk of war and terrorist attacks in which refineries and fuel transportation grids may be targeted, higher pressures are exerted on refiners to optimize performance and find the best combination of feed and processes to produce salable products that meet stricter product specifications, while at the same time meeting refinery supply commitments and of course making profit. This is done through multi objective optimization. For corporate refining companies and at the national level, Intea-Refinery and Inter-Refinery optimization is the second step in optimizing the operation of the whole refining chain as a single system. Most refinery-wide optimization methods do not cover multiple objectives such as minimizing environmental impact, avoiding catastrophic failures, or enhancing product spec upgrade effects. This work starts by carrying out a refinery-wide, single objective optimization, and then moves to multi objective-single refinery optimization. The last step is multi objective-multi refinery optimization, the objectives of which are analysis of the effects of economic, environmental, product spec, strategic, and catastrophic failure. Simulation runs were carried out using both MATLAB and ASPEN PIMS utilizing nonlinear techniques to solve the optimization problem. The results addressed the need to debottleneck some refineries or transportation media in order to meet the demand for essential products under partial or total failure scenarios. They also addressed how importing some high spec products can help recover some of the losses and what is needed in order to accomplish this. In addition, the results showed nonlinear relations among local and global objectives for some refineries. The results demonstrate that refineries can have a local multi objective optimum that does not

  7. XTALOPT: An open-source evolutionary algorithm for crystal structure prediction

    Science.gov (United States)

    Lonie, David C.; Zurek, Eva

    2011-02-01

    The implementation and testing of XTALOPT, an evolutionary algorithm for crystal structure prediction, is outlined. We present our new periodic displacement (ripple) operator which is ideally suited to extended systems. It is demonstrated that hybrid operators, which combine two pure operators, reduce the number of duplicate structures in the search. This allows for better exploration of the potential energy surface of the system in question, while simultaneously zooming in on the most promising regions. A continuous workflow, which makes better use of computational resources as compared to traditional generation based algorithms, is employed. Various parameters in XTALOPT are optimized using a novel benchmarking scheme. XTALOPT is available under the GNU Public License, has been interfaced with various codes commonly used to study extended systems, and has an easy to use, intuitive graphical interface. Program summaryProgram title:XTALOPT Catalogue identifier: AEGX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 36 849 No. of bytes in distributed program, including test data, etc.: 1 149 399 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux Classification: 7.7 External routines: QT [2], OpenBabel [3], AVOGADRO [4], SPGLIB [8] and one of: VASP [5], PWSCF [6], GULP [7]. Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum on their potential energy surface. Our evolutionary algorithm, XTALOPT, is freely

  8. Multi-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problems

    International Nuclear Information System (INIS)

    Yousefi, Moslem; Darus, Amer Nordin; Yousefi, Milad; Hooshyar, Danial

    2015-01-01

    The complicated task of design optimization of compact heat exchangers (CHEs) have been effectively performed by using evolutionary algorithms (EAs) in the recent years. However, mainly due to difficulties of handling extra variables, the design approach has been based on constant rates of heat duty in the available literature. In this paper, a new design strategy is presented where variable operating conditions, which better represent real-world problems, are considered. The proposed strategy is illustrated using a case study for design of a plate-fin heat exchanger though it can be employed for all types of heat exchangers without much change. Learning automata based particle swarm optimization (LAPSO), is employed for handling nine design variables while satisfying various equality and inequality constraints. For handling the constraints, a novel feasibility based ranking strategy (FBRS) is introduced. The numerical results indicate that the design based on variable heat duties yields in more cost savings and superior thermodynamics efficiency comparing to a conventional design approach. Furthermore, the proposed algorithm has shown a superior performance in finding the near-optimum solution for this task when it is compared to the most popular evolutionary algorithms in engineering applications, i.e. genetic algorithm (GA) and particle swarm optimization (PSO). - Highlights: • Multi-stage design of heat exchangers is presented. • Feasibility based ranking strategy is employed for constraint handling. • Learning abilities added to particle swarm optimization

  9. Contribution to the evaluation and to the improvement of multi-objective optimization methods: application to the optimization of nuclear fuel reloading pattern

    International Nuclear Information System (INIS)

    Collette, Y.

    2002-01-01

    methods called MOCOSA and NSCOSA mainly based on the COSA method, which simulated a genetic algorithm by just using tools from the simulated annealing and, therefore, without crossover operator. The MOCOSA and NSCOSA methods uses tools from the MOGA and NSGA methods based on genetic algorithms. An other problem related to multi-objective optimization is the problem of data visualization. A redundant type of multi-objective problem is treated in scientific literature: the bi-objective problem, easy to illustrate. We propose, in this thesis, some methods allowing to visualize solutions set of arbitrary dimensions and, particularly, the MCDM method ('Multi-objective Concordance Discordance Mapping') which transforms a real multi-objective optimization problem (a problem which has more than two objective functions) in a simpler bi-objective problem. We have also defined new multidimensional transformation methods that are able to conserve a relation of order (such as dominance relation). The application of this transformation gives birth to the MCDM PC method ('Multi-objective Concordance Discordance Mapping Pareto Conservative'). Moreover, we have defined a new classification of multi-objective optimization methods with the goal to ease the choice of a multi-objective optimization method to solve a given problem. To focus this classification, we have extracted from multi-objective optimization methods the most important elements and we have organized these elements as a hierarchy. The 'navigation' through this hierarchy is done through some simple questions asked to the user, in direct relationship to the given problem. These results are applied to the multi-objective optimization of nuclear core reload pattern, which is composed of security constraints and economic criteria. This combinatorial optimization problem can be illustrated by using a check covered by pawns where a pawn corresponds to a nuclear assembly. The goal is to find a distribution of pawns so as to minimize

  10. Biokinetic model-based multi-objective optimization of Dunaliella tertiolecta cultivation using elitist non-dominated sorting genetic algorithm with inheritance.

    Science.gov (United States)

    Sinha, Snehal K; Kumar, Mithilesh; Guria, Chandan; Kumar, Anup; Banerjee, Chiranjib

    2017-10-01

    Algal model based multi-objective optimization using elitist non-dominated sorting genetic algorithm with inheritance was carried out for batch cultivation of Dunaliella tertiolecta using NPK-fertilizer. Optimization problems involving two- and three-objective functions were solved simultaneously. The objective functions are: maximization of algae-biomass and lipid productivity with minimization of cultivation time and cost. Time variant light intensity and temperature including NPK-fertilizer, NaCl and NaHCO 3 loadings are the important decision variables. Algal model involving Monod/Andrews adsorption kinetics and Droop model with internal nutrient cell quota was used for optimization studies. Sets of non-dominated (equally good) Pareto optimal solutions were obtained for the problems studied. It was observed that time variant optimal light intensity and temperature trajectories, including optimum NPK fertilizer, NaCl and NaHCO 3 concentration has significant influence to improve biomass and lipid productivity under minimum cultivation time and cost. Proposed optimization studies may be helpful to implement the control strategy in scale-up operation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

    Science.gov (United States)

    Xu, Gongxian; Liu, Ying; Gao, Qunwang

    2016-02-10

    This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. A New Evolutionary Algorithm Based on Bacterial Evolution and Its Application for Scheduling A Flexible Manufacturing System

    Directory of Open Access Journals (Sweden)

    Chandramouli Anandaraman

    2012-01-01

    Full Text Available A new evolutionary computation algorithm, Superbug algorithm, which simulates evolution of bacteria in a culture, is proposed. The algorithm is developed for solving large scale optimization problems such as scheduling, transportation and assignment problems. In this work, the algorithm optimizes machine schedules in a Flexible Manufacturing System (FMS by minimizing makespan. The FMS comprises of four machines and two identical Automated Guided Vehicles (AGVs. AGVs are used for carrying jobs between the Load/Unload (L/U station and the machines. Experimental results indicate the efficiency of the proposed algorithm in its optimization performance in scheduling is noticeably superior to other evolutionary algorithms when compared to the best results reported in the literature for FMS Scheduling.

  13. Multicontroller: an object programming approach to introduce advanced control algorithms for the GCS large scale project

    CERN Document Server

    Cabaret, S; Coppier, H; Rachid, A; Barillère, R; CERN. Geneva. IT Department

    2007-01-01

    The GCS (Gas Control System) project team at CERN uses a Model Driven Approach with a Framework - UNICOS (UNified Industrial COntrol System) - based on PLC (Programming Language Controller) and SCADA (Supervisory Control And Data Acquisition) technologies. The first' UNICOS versions were able to provide a PID (Proportional Integrative Derivative) controller whereas the Gas Systems required more advanced control strategies. The MultiController is a new UNICOS object which provides the following advanced control algorithms: Smith Predictor, PFC (Predictive Function Control), RST* and GPC (Global Predictive Control). Its design is based on a monolithic entity with a global structure definition which is able to capture the desired set of parameters of any specific control algorithm supported by the object. The SCADA system -- PVSS - supervises the MultiController operation. The PVSS interface provides users with supervision faceplate, in particular it links any MultiController with recipes: the GCS experts are ab...

  14. SYNTHESIS AND REDUCED LOGIC GATE REALIZATION OF MULTI-VALUED LOGIC FUNCTIONS USING NEURAL NETWORK DEPLOYMENT ALGORITHM

    Directory of Open Access Journals (Sweden)

    A. K. CHOWDHURY

    2016-02-01

    Full Text Available In this paper an evolutionary technique for synthesizing Multi-Valued Logic (MVL functions using Neural Network Deployment Algorithm (NNDA is presented. The algorithm is combined with back-propagation learning capability and neural MVL operators. This research article is done to observe the anomalistic characteristics of MVL neural operators and their role in synthesis. The advantages of NNDA-MVL algorithm is demonstrated with realization of synthesized many valued functions with lesser MVL operators. The characteristic feature set consists of MVL gate count, network link count, network propagation delay and accuracy achieved in training. In brief, this paper depicts an effort of reduced network size for synthesized MVL functions. Trained MVL operators improve the basic architecture by reducing MIN gate and interlink connection by 52.94% and 23.38% respectively.

  15. An Evolutionary Approach for Optimizing Hierarchical Multi-Agent System Organization

    OpenAIRE

    Shen, Zhiqi; Yu, Ling; Yu, Han

    2014-01-01

    It has been widely recognized that the performance of a multi-agent system is highly affected by its organization. A large scale system may have billions of possible ways of organization, which makes it impractical to find an optimal choice of organization using exhaustive search methods. In this paper, we propose a genetic algorithm aided optimization scheme for designing hierarchical structures of multi-agent systems. We introduce a novel algorithm, called the hierarchical genetic algorithm...

  16. Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.

    Science.gov (United States)

    Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard

    2012-06-07

    We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.

  17. Hybrid Evolutionary Metaheuristics for Concurrent Multi-Objective Design of Urban Road and Public Transit Networks

    NARCIS (Netherlands)

    Miandoabchi, Elnaz; Farahani, Reza Zanjirani; Dullaert, Wout; Szeto, W. Y.

    This paper addresses a bi-modal multi-objective discrete urban road network design problem with automobile and bus flow interaction. The problem considers the concurrent urban road and bus network design in which the authorities play a major role in designing bus network topology. The road network

  18. Evaluating ortholog prediction algorithms in a yeast model clade.

    Directory of Open Access Journals (Sweden)

    Leonidas Salichos

    Full Text Available BACKGROUND: Accurate identification of orthologs is crucial for evolutionary studies and for functional annotation. Several algorithms have been developed for ortholog delineation, but so far, manually curated genome-scale biological databases of orthologous genes for algorithm evaluation have been lacking. We evaluated four popular ortholog prediction algorithms (MultiParanoid; and OrthoMCL; RBH: Reciprocal Best Hit; RSD: Reciprocal Smallest Distance; the last two extended into clustering algorithms cRBH and cRSD, respectively, so that they can predict orthologs across multiple taxa against a set of 2,723 groups of high-quality curated orthologs from 6 Saccharomycete yeasts in the Yeast Gene Order Browser. RESULTS: Examination of sensitivity [TP/(TP+FN], specificity [TN/(TN+FP], and accuracy [(TP+TN/(TP+TN+FP+FN] across a broad parameter range showed that cRBH was the most accurate and specific algorithm, whereas OrthoMCL was the most sensitive. Evaluation of the algorithms across a varying number of species showed that cRBH had the highest accuracy and lowest false discovery rate [FP/(FP+TP], followed by cRSD. Of the six species in our set, three descended from an ancestor that underwent whole genome duplication. Subsequent differential duplicate loss events in the three descendants resulted in distinct classes of gene loss patterns, including cases where the genes retained in the three descendants are paralogs, constituting 'traps' for ortholog prediction algorithms. We found that the false discovery rate of all algorithms dramatically increased in these traps. CONCLUSIONS: These results suggest that simple algorithms, like cRBH, may be better ortholog predictors than more complex ones (e.g., OrthoMCL and MultiParanoid for evolutionary and functional genomics studies where the objective is the accurate inference of single-copy orthologs (e.g., molecular phylogenetics, but that all algorithms fail to accurately predict orthologs when paralogy

  19. The Application of Fitness Sharing Method in Evolutionary Algorithm to Optimizing the Travelling Salesman Problem (TSP

    Directory of Open Access Journals (Sweden)

    Nurmaulidar Nurmaulidar

    2015-04-01

    Full Text Available Travelling Salesman Problem (TSP is one of complex optimization problem that is difficult to be solved, and require quite a long time for a large number of cities. Evolutionary algorithm is a precise algorithm used in solving complex optimization problem as it is part of heuristic method. Evolutionary algorithm, like many other algorithms, also experiences a premature convergence phenomenon, whereby variation is eliminated from a population of fairly fit individuals before a complete solution is achieved. Therefore it requires a method to delay the convergence. A specific method of fitness sharing called phenotype fitness sharing has been used in this research. The aim of this research is to find out whether fitness sharing in evolutionary algorithm is able to optimize TSP. There are two concepts of evolutionary algorithm being used in this research. the first one used single elitism and the other one used federated solution. The two concepts had been tested to the method of fitness sharing by using the threshold of 0.25, 0.50 and 0.75. The result was then compared to a non fitness sharing method. The result in this study indicated that by using single elitism concept, fitness sharing was able to give a more optimum result for the data of 100-1000 cities. On the other hand, by using federation solution concept, fitness sharing can yield a more optimum result for the data above 1000 cities, as well as a better solution of data-spreading compared to the method without fitness sharing.

  20. A Global Multi-Objective Optimization Tool for Design of Mechatronic Components using Generalized Differential Evolution

    DEFF Research Database (Denmark)

    Bech, Michael Møller; Nørgård, Christian; Roemer, Daniel Beck

    2016-01-01

    This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri-objectiv......This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri...... different optimization control parameter settings and it is concluded that GDE3 is a reliable optimization tool that can assist mechatronic engineers in the design and decision making process....

  1. RAMS+C informed decision-making with application to multi-objective optimization of technical specifications and maintenance using genetic algorithms

    International Nuclear Information System (INIS)

    Martorell, S.; Villanueva, J.F.; Carlos, S.; Nebot, Y.; Sanchez, A.; Pitarch, J.L.; Serradell, V.

    2005-01-01

    The role of technical specifications and maintenance (TSM) activities at nuclear power plants (NPP) aims to increase reliability, availability and maintainability (RAM) of Safety-Related Equipment, which, in turn, must yield to an improved level of plant safety. However, more resources (e.g. costs, task force, etc.) have to be assigned in above areas to achieve better scores in reliability, availability, maintainability and safety (RAMS). Current situation at NPP shows different programs implemented at the plant that aim to the improvement of particular TSM-related parameters where the decision-making process is based on the assessment of the impact of the change proposed on a subgroup of RAMS+C attributes. This paper briefly reviews the role of TSM and two main groups of improvement programs at NPP, which suggest the convenience of considering the approach proposed in this paper for the Integrated Multi-Criteria Decision-Making on changes to TSM-related parameters based on RAMS+C criteria as a whole, as it can be seem as a decision-making process more consistent with the role and synergic effects of TSM and the objectives and goals of current improvement programs at NPP. The case of application to the Emergency Diesel Generator system demonstrates the viability and significance of the proposed approach for the Multi-objective Optimization of TSM-related parameters using a Genetic Algorithm

  2. C-mixture and multi-constraints based genetic algorithm for collaborative data publishing

    Directory of Open Access Journals (Sweden)

    Yogesh R. Kulkarni

    2018-04-01

    Full Text Available Due to increasing need of using distributed databases, high demand presents on sharing data to easily update and access the useful information without any interruption. The sharing of distributed databases causes a serious issue of securing information since the databases consist of sensitive personal information. To preserve the sensitive information and at the same time, releasing the useful information, a significant effort is made by the researchers under privacy preserving data publishing that have been receiving considerable attention in recent years. In this work, a new privacy measure, called c-mixture is introduced to maintain the privacy constraint without affecting utility of the database. In order to apply the proposed privacy measure to privacy preserving data publishing, a new algorithm called, CPGEN is developed using genetic algorithm and multi-objective constraints. The proposed multi-objective optimization considered the multiple privacy constraints along with the utility measurement to measure the importance. Also, the proposed CPGEN is adapted to handle the cold-start problem which commonly happened in distributed databases. The proposed algorithm is experimented with adult dataset and quantitative performance is analyzed using generalized information loss and average equivalence class size metric. From the experimentation, we proved that the proposed algorithm maintained the privacy and utility as compared with the existing algorithm. Keywords: Privacy, Utility, Distributed databases, Data publishing, Optimization, Sensitive information

  3. An Evolutionary Algorithm for Multiobjective Fuzzy Portfolio Selection Models with Transaction Cost and Liquidity

    Directory of Open Access Journals (Sweden)

    Wei Yue

    2015-01-01

    Full Text Available The major issues for mean-variance-skewness models are the errors in estimations that cause corner solutions and low diversity in the portfolio. In this paper, a multiobjective fuzzy portfolio selection model with transaction cost and liquidity is proposed to maintain the diversity of portfolio. In addition, we have designed a multiobjective evolutionary algorithm based on decomposition of the objective space to maintain the diversity of obtained solutions. The algorithm is used to obtain a set of Pareto-optimal portfolios with good diversity and convergence. To demonstrate the effectiveness of the proposed model and algorithm, the performance of the proposed algorithm is compared with the classic MOEA/D and NSGA-II through some numerical examples based on the data of the Shanghai Stock Exchange Market. Simulation results show that our proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms and the proposed model can maintain quite well the diversity of portfolio. The purpose of this paper is to deal with portfolio problems in the weighted possibilistic mean-variance-skewness (MVS and possibilistic mean-variance-skewness-entropy (MVS-E frameworks with transaction cost and liquidity and to provide different Pareto-optimal investment strategies as diversified as possible for investors at a time, rather than one strategy for investors at a time.

  4. A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

    Directory of Open Access Journals (Sweden)

    Nazia Anwar

    2018-03-01

    Full Text Available Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of scientific workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO. The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT, in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs. The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm.

  5. Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling

    Science.gov (United States)

    Srivastav, Roshan; Srinivasan, K.; Sudheer, K. P.

    2016-11-01

    A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is

  6. A chaos-based evolutionary algorithm for general nonlinear programming problems

    International Nuclear Information System (INIS)

    El-Shorbagy, M.A.; Mousa, A.A.; Nasr, S.M.

    2016-01-01

    In this paper we present a chaos-based evolutionary algorithm (EA) for solving nonlinear programming problems named chaotic genetic algorithm (CGA). CGA integrates genetic algorithm (GA) and chaotic local search (CLS) strategy to accelerate the optimum seeking operation and to speed the convergence to the global solution. The integration of global search represented in genetic algorithm and CLS procedures should offer the advantages of both optimization methods while offsetting their disadvantages. By this way, it is intended to enhance the global convergence and to prevent to stick on a local solution. The inherent characteristics of chaos can enhance optimization algorithms by enabling it to escape from local solutions and increase the convergence to reach to the global solution. Twelve chaotic maps have been analyzed in the proposed approach. The simulation results using the set of CEC’2005 show that the application of chaotic mapping may be an effective strategy to improve the performances of EAs.

  7. Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

    Directory of Open Access Journals (Sweden)

    Paul Jean Etienne Jeszensky

    2005-02-01

    Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.

  8. Approximating multi-objective scheduling problems

    NARCIS (Netherlands)

    Dabia, S.; Talbi, El-Ghazali; Woensel, van T.; Kok, de A.G.

    2013-01-01

    In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to

  9. High performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systems

    KAUST Repository

    Abdelfattah, Ahmad; Gendron, É ric; Gratadour, Damien; Keyes, David E.; Ltaief, Hatem; Sevin, Arnaud; Vidal, Fabrice

    2014-01-01

    Multi-object adaptive optics (MOAO) is a novel adaptive optics (AO) technique dedicated to the special case of wide-field multi-object spectrographs (MOS). It applies dedicated wavefront corrections to numerous independent tiny patches spread over a large field of view (FOV). The control of each deformable mirror (DM) is done individually using a tomographic reconstruction of the phase based on measurements from a number of wavefront sensors (WFS) pointing at natural and artificial guide stars in the field. The output of this study helps the design of a new instrument called MOSAIC, a multi-object spectrograph proposed for the European Extremely Large Telescope (E-ELT). We have developed a novel hybrid pseudo-analytical simulation scheme that allows us to accurately simulate in detail the tomographic problem. The main challenge resides in the computation of the tomographic reconstructor, which involves pseudo-inversion of a large dense symmetric matrix. The pseudo-inverse is computed using an eigenvalue decomposition, based on the divide and conquer algorithm, on multicore systems with multi-GPUs. Thanks to a new symmetric matrix-vector product (SYMV) multi-GPU kernel, our overall implementation scores significant speedups over standard numerical libraries on multicore, like Intel MKL, and up to 60% speedups over the standard MAGMA implementation on 8 Kepler K20c GPUs. At 40,000 unknowns, this appears to be the largest-scale tomographic AO matrix solver submitted to computation, to date, to our knowledge and opens new research directions for extreme scale AO simulations. © 2014 Springer International Publishing Switzerland.

  10. Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

    Science.gov (United States)

    Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

    2018-04-01

    This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

  11. 2nd International Conference on Harmony Search Algorithm

    CERN Document Server

    Geem, Zong

    2016-01-01

    The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft computing, an important paradigm in the science and engineering community.  This volume, the proceedings of the 2nd International Conference on Harmony Search Algorithm 2015 (ICHSA 2015), brings together contributions describing the latest developments in the field of soft computing with a special focus on HSA techniques. It includes coverage of new methods that have potentially immense application in various fields. Contributed articles cover aspects of the following topics related to the Harmony Search Algorithm: analytical studies; improved, hybrid and multi-objective variants; parameter tuning; and large-scale applications.  The book also contains papers discussing recent advances on the following topics: genetic algorithms; evolutionary strategies; the firefly algorithm and cuckoo search; particle swarm optimization and ant colony optimization; simulated annealing; and local search techniques.   This book ...

  12. Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization

    International Nuclear Information System (INIS)

    Zhang, Enze; Chen, Qingwei

    2016-01-01

    Most of the existing works addressing reliability redundancy allocation problems are based on the assumption of fixed reliabilities of components. In real-life situations, however, the reliabilities of individual components may be imprecise, most often given as intervals, under different operating or environmental conditions. This paper deals with reliability redundancy allocation problems modeled in an interval environment. An interval multi-objective optimization problem is formulated from the original crisp one, where system reliability and cost are simultaneously considered. To render the multi-objective particle swarm optimization (MOPSO) algorithm capable of dealing with interval multi-objective optimization problems, a dominance relation for interval-valued functions is defined with the help of our newly proposed order relations of interval-valued numbers. Then, the crowding distance is extended to the multi-objective interval-valued case. Finally, the effectiveness of the proposed approach has been demonstrated through two numerical examples and a case study of supervisory control and data acquisition (SCADA) system in water resource management. - Highlights: • We model the reliability redundancy allocation problem in an interval environment. • We apply the particle swarm optimization directly on the interval values. • A dominance relation for interval-valued multi-objective functions is defined. • The crowding distance metric is extended to handle imprecise objective functions.

  13. A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization

    International Nuclear Information System (INIS)

    Zhang, Enze; Wu, Yifei; Chen, Qingwei

    2014-01-01

    This paper proposes a practical approach, combining bare-bones particle swarm optimization and sensitivity-based clustering for solving multi-objective reliability redundancy allocation problems (RAPs). A two-stage process is performed to identify promising solutions. Specifically, a new bare-bones multi-objective particle swarm optimization algorithm (BBMOPSO) is developed and applied in the first stage to identify a Pareto-optimal set. This algorithm mainly differs from other multi-objective particle swarm optimization algorithms in the parameter-free particle updating strategy, which is especially suitable for handling the complexity and nonlinearity of RAPs. Moreover, by utilizing an approach based on the adaptive grid to update the global particle leaders, a mutation operator to improve the exploration ability and an effective constraint handling strategy, the integrated BBMOPSO algorithm can generate excellent approximation of the true Pareto-optimal front for RAPs. This is followed by a data clustering technique based on difference sensitivity in the second stage to prune the obtained Pareto-optimal set and obtain a small, workable sized set of promising solutions for system implementation. Two illustrative examples are presented to show the feasibility and effectiveness of the proposed approach

  14. Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem

    Directory of Open Access Journals (Sweden)

    Susanta Dutta

    2018-05-01

    Full Text Available This paper presents an efficient quasi-oppositional chemical reaction optimization (QOCRO technique to find the feasible optimal solution of the multi objective optimal reactive power dispatch (RPD problem with flexible AC transmission system (FACTS device. The quasi-oppositional based learning (QOBL is incorporated in conventional chemical reaction optimization (CRO, to improve the solution quality and the convergence speed. To check the superiority of the proposed method, it is applied on IEEE 14-bus and 30-bus systems and the simulation results of the proposed approach are compared to those reported in the literature. The computational results reveal that the proposed algorithm has excellent convergence characteristics and is superior to other multi objective optimization algorithms. Keywords: Quasi-oppositional chemical reaction optimization (QOCRO, Reactive power dispatch (RPD, TCSC, SVC, Multi-objective optimization

  15. Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

    Science.gov (United States)

    Zatarain Salazar, Jazmin; Reed, Patrick M.; Quinn, Julianne D.; Giuliani, Matteo; Castelletti, Andrea

    2017-11-01

    Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a

  16. Self-organized modularization in evolutionary algorithms.

    Science.gov (United States)

    Dauscher, Peter; Uthmann, Thomas

    2005-01-01

    The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

  17. Multi-Objective Motion Control Optimization for the Bridge Crane System

    Directory of Open Access Journals (Sweden)

    Renxin Xiao

    2018-03-01

    Full Text Available A novel control algorithm combining the linear quadratic regulator (LQR control and trajectory planning (TP is proposed for the control of an underactuated crane system, targeting position adjustment and swing suppression. The TP is employed to control the swing angle within certain constraints, and the LQR is applied to achieve anti-disturbance. In order to improve the accuracy of the position control, a differential-integral control loop is applied. The weighted LQR matrices representing priorities of the state variables for the bridge crane motion are searched by the multi-objective genetic algorithm (MOGA. The stability proof is provided in order to validate the effectiveness of the proposed algorithm. Numerous simulation and experimental validations justify the feasibility of the proposed method.

  18. A Comparison of Evolutionary Algorithms for Tracking Time-Varying Recursive Systems

    Directory of Open Access Journals (Sweden)

    White Michael S

    2003-01-01

    Full Text Available A comparison is made of the behaviour of some evolutionary algorithms in time-varying adaptive recursive filter systems. Simulations show that an algorithm including random immigrants outperforms a more conventional algorithm using the breeder genetic algorithm as the mutation operator when the time variation is discontinuous, but neither algorithm performs well when the time variation is rapid but smooth. To meet this deficit, a new hybrid algorithm which uses a hill climber as an additional genetic operator, applied for several steps at each generation, is introduced. A comparison is made of the effect of applying the hill climbing operator a few times to all members of the population or a larger number of times solely to the best individual; it is found that applying to the whole population yields the better results, substantially improved compared with those obtained using earlier methods.

  19. Performance of a genetic algorithm for solving the multi-objective, multimodel transportation network design problem

    NARCIS (Netherlands)

    Brands, Ties; van Berkum, Eric C.

    2014-01-01

    The optimization of infrastructure planning in a multimodal network is defined as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train

  20. Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Xiang Yu

    2016-06-01

    Full Text Available Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system’s economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.