8th International Conference on Multi-Objective and Goal Programming

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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.

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

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

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

2017-07-01

problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future. Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality. Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

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

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Muhammad Hashim

2017-05-01

solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future. Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality. Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

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

problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future. Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality. Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

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

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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.

FGP Approach for Solving Multi-level Multi-objective Quadratic Fractional Programming Problem with Fuzzy parameters

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m. s. osman

2017-09-01

Full Text Available In this paper, we consider fuzzy goal programming (FGP approach for solving multi-level multi-objective quadratic fractional programming (ML-MOQFP problem with fuzzy parameters in the constraints. Firstly, the concept of the ?-cut approach is applied to transform the set of fuzzy constraints into a common deterministic one. Then, the quadratic fractional objective functions in each level are transformed into quadratic objective functions based on a proposed transformation. Secondly, the FGP approach is utilized to obtain a compromise solution for the ML-MOQFP problem by minimizing the sum of the negative deviational variables. Finally, an illustrative numerical example is given to demonstrate the applicability and performance of the proposed approach.

A multi-objective programming model for assessment the GHG emissions in MSW management

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Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr [National Technical University of Athens, Iroon Polytechniou 9, Zografou, Athens, 15780 (Greece); Skoulaxinou, Sotiria [EPEM SA, 141 B Acharnon Str., Athens, 10446 (Greece); Gakis, Nikos [FACETS SA, Agiou Isidorou Str., Athens, 11471 (Greece); Katsouros, Vassilis [Athena Research and Innovation Center, Artemidos 6 and Epidavrou Str., Maroussi, 15125 (Greece); Georgopoulou, Elena [National Observatory of Athens, Thisio, Athens, 11810 (Greece)

2013-09-15

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the

A multi-objective programming model for assessment the GHG emissions in MSW management

International Nuclear Information System (INIS)

Mavrotas, George; Skoulaxinou, Sotiria; Gakis, Nikos; Katsouros, Vassilis; Georgopoulou, Elena

2013-01-01

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH 4 generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application

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

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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.

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

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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.

Optimum analysis of pavement maintenance using multi-objective genetic algorithms

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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.

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

Fuzzy preference based interactive fuzzy physical programming and its application in multi-objective optimization

International Nuclear Information System (INIS)

Zhang, Xu; Huang, Hong Zhong; Yu, Lanfeng

2006-01-01

Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer

Fuzzy Multi Objective Linear Programming Problem with Imprecise Aspiration Level and Parameters

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Zahra Shahraki

2015-07-01

Full Text Available This paper considers the multi-objective linear programming problems with fuzzygoal for each of the objective functions and constraints. Most existing works deal withlinear membership functions for fuzzy goals. In this paper, exponential membershipfunction is used.

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

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Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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.

Genetic programming theory and practice X

CERN Document Server

Riolo, Rick; Ritchie, Marylyn D; Moore, Jason H

2013-01-01

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of  injecting

Stability of multi-objective bi-level linear programming problems under fuzziness

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Abo-Sinna Mahmoud A.

2013-01-01

Full Text Available This paper deals with multi-objective bi-level linear programming problems under fuzzy environment. In the proposed method, tentative solutions are obtained and evaluated by using the partial information on preference of the decision-makers at each level. The existing results concerning the qualitative analysis of some basic notions in parametric linear programming problems are reformulated to study the stability of multi-objective bi-level linear programming problems. An algorithm for obtaining any subset of the parametric space, which has the same corresponding Pareto optimal solution, is presented. Also, this paper established the model for the supply-demand interaction in the age of electronic commerce (EC. First of all, the study uses the individual objectives of both parties as the foundation of the supply-demand interaction. Subsequently, it divides the interaction, in the age of electronic commerce, into the following two classifications: (i Market transactions, with the primary focus on the supply demand relationship in the marketplace; and (ii Information service, with the primary focus on the provider and the user of information service. By applying the bi-level programming technique of interaction process, the study will develop an analytical process to explain how supply-demand interaction achieves a compromise or why the process fails. Finally, a numerical example of information service is provided for the sake of illustration.

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

: Unit Hydrograph, Entropy, Grade of Stage Gauge Station, Multi Objective Genetic Algorithm(MOGA), Optimal Stage Guage Network Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)

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

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

Towards lexicographic multi-objective linear programming using grossone methodology

Science.gov (United States)

Cococcioni, Marco; Pappalardo, Massimo; Sergeyev, Yaroslav D.

2016-10-01

Lexicographic Multi-Objective Linear Programming (LMOLP) problems can be solved in two ways: preemptive and nonpreemptive. The preemptive approach requires the solution of a series of LP problems, with changing constraints (each time the next objective is added, a new constraint appears). The nonpreemptive approach is based on a scalarization of the multiple objectives into a single-objective linear function by a weighted combination of the given objectives. It requires the specification of a set of weights, which is not straightforward and can be time consuming. In this work we present both mathematical and software ingredients necessary to solve LMOLP problems using a recently introduced computational methodology (allowing one to work numerically with infinities and infinitesimals) based on the concept of grossone. The ultimate goal of such an attempt is an implementation of a simplex-like algorithm, able to solve the original LMOLP problem by solving only one single-objective problem and without the need to specify finite weights. The expected advantages are therefore obvious.

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.

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.

Intersection signal control multi-objective optimization based on genetic algorithm

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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.

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

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.

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

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Narong Wichapa

2018-01-01

Full Text Available Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any stand-alone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in Upper-Northeastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP. After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP, namely the HGP model, was tested. Finally, the vehicle routing problem (VRP for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA which hybridizes the push forward insertion heuristic (PFIH, genetic algorithm (GA and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles

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.

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.

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

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...

An inexact multi-objective programming approach for strategic environmental assessment on regional development plan

Institute of Scientific and Technical Information of China (English)

WANG Jihua; GUO Huaicheng; LIU Lei; HAO Mingjia; ZHANG Ming; LU Xiaojian; XING Kexia

2004-01-01

This paper presents the development of an inexact multi-objective programming (IMOP) model and its application to the strategic environmental assessment (SEA) for the regional development plan for the Hunnan New Zone (HNZ) in Shenyang City, China. Inexact programming and multi-objective programming methods are employed to effectively account for extensive uncertainties in the study system and to reflect various interests from different stakeholders, respectively. In the case study, balancing-economy-and-environment scenario and focusing-industry-development scenario are analyzed by the interactive solution process for addressing the preferences from local authorities and compromises among different objectives. Through interpreting the model solutions under both scenarios, analysis of industrial structure, waste water treatment plant(WWTP) expansion, water consumption and pollution generation and treatment are undertaken for providing a solid base to justify and evaluate the HNZ regional development plan. The study results show that the developed IMOP-SEA framework is feasible and applicable in carrying comprehensive environmental impact assessments for development plan in a more effective and efficient manner.

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.

Multiple utility constrained multi-objective programs using Bayesian theory

Science.gov (United States)

Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

2018-03-01

A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

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.

New approach for solving intuitionistic fuzzy multi-objective ...

Indian Academy of Sciences (India)

SANKAR KUMAR ROY

2018-02-07

Feb 7, 2018 ... Transportation problem; multi-objective decision making; intuitionistic fuzzy programming; interval programming ... MOTP under multi-choice environment using utility func- ... theory is an intuitionistic fuzzy set (IFS), which was.

From Single- to Multi-Objective Auto-Tuning of Programs: Advantages and Implications

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Juan Durillo

2014-01-01

Full Text Available Automatic tuning (auto-tuning of software has emerged in recent years as a promising method that tries to automatically adapt the behaviour of a program to attain different performance objectives on a given computing system. This method is gaining momentum due to the increasing complexity of modern multicore-based hardware architectures. Many solutions to auto-tuning have been explored ranging from simple random search to more sophisticate methods like machine learning or evolutionary search. To this day, it is still unclear whether these approaches are general enough to encompass all the complexities of the problem (e.g. search space, parameters influencing the search space, input data sensitivity, etc., or which approach is best suited for a given problem. Furthermore, the growing interest in auto-tuning a program for several objectives is increasing this confusion even further. The goal of this paper is to formally describe the problem addressed by auto-tuning programs and review existing solutions highlighting the advantages and drawbacks of different techniques for single-objective as well as multi-objective auto-tuning approaches.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

Science.gov (United States)

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

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

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.

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

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.

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Directory of Open Access Journals (Sweden)

Hossein Yousefi

2017-06-01

Full Text Available A vehicle routing problem with time windows (VRPTW is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functions that should be considered. This paper considers not only the minimum distance and the number of vehicles used as the objective function, the customer’s satisfaction with the priority of customers is also considered. Additionally, it presents a new model for a bi-objective VRPTW solved by a revised multi-choice goal programming approach, in which the decision maker determines optimistic aspiration levels for each objective function. Two meta-heuristic methods, namely simulated annealing (SA and genetic algorithm (GA, are proposed to solve large-sized problems. Moreover, the experimental design is used to tune the parameters of the proposed algorithms. The presented model is verified by a real-world case study and a number of test problems. The computational results verify the efficiency of the proposed SA and GA.

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

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.

A linear bi-level multi-objective program for optimal allocation of water resources.

Directory of Open Access Journals (Sweden)

Ijaz Ahmad

Full Text Available This paper presents a simple bi-level multi-objective linear program (BLMOLP with a hierarchical structure consisting of reservoir managers and several water use sectors under a multi-objective framework for the optimal allocation of limited water resources. Being the upper level decision makers (i.e., leader in the hierarchy, the reservoir managers control the water allocation system and tend to create a balance among the competing water users thereby maximizing the total benefits to the society. On the other hand, the competing water use sectors, being the lower level decision makers (i.e., followers in the hierarchy, aim only to maximize individual sectoral benefits. This multi-objective bi-level optimization problem can be solved using the simultaneous compromise constraint (SICCON technique which creates a compromise between upper and lower level decision makers (DMs, and transforms the multi-objective function into a single decision-making problem. The bi-level model developed in this study has been applied to the Swat River basin in Pakistan for the optimal allocation of water resources among competing water demand sectors and different scenarios have been developed. The application of the model in this study shows that the SICCON is a simple, applicable and feasible approach to solve the BLMOLP problem. Finally, the comparisons of the model results show that the optimization model is practical and efficient when it is applied to different conditions with priorities assigned to various water users.

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.

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

Directory of Open Access Journals (Sweden)

Kaveh Khalili-Damghani

2017-07-01

Full Text Available In this paper a multi-period multi-product multi-objective aggregate production planning (APP model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.

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)

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.

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.

7 CFR 3560.602 - Program objectives.

Science.gov (United States)

2010-01-01

... AGRICULTURE DIRECT MULTI-FAMILY HOUSING LOANS AND GRANTS On-Farm Labor Housing § 3560.602 Program objectives. In addition to the objectives stated in § 3560.52, on-farm labor housing funds will be used to... 7 Agriculture 15 2010-01-01 2010-01-01 false Program objectives. 3560.602 Section 3560.602...

7 CFR 3560.552 - Program objectives.

Science.gov (United States)

2010-01-01

... AGRICULTURE DIRECT MULTI-FAMILY HOUSING LOANS AND GRANTS Off-Farm Labor Housing § 3560.552 Program objectives. (a) In addition to the objectives stated in § 3560.52, off-farm labor housing loan and grant funds... 7 Agriculture 15 2010-01-01 2010-01-01 false Program objectives. 3560.552 Section 3560.552...

Developing a Novel Multi-objective Programming Model for Personnel Assignment Problem

Directory of Open Access Journals (Sweden)

Mehdi Seifbarghy

2014-05-01

Full Text Available The assignment of personnel to the right positions in order to increase organization's performance is one of the most crucial tasks in human resource management. In this paper, personnel assignment problem is formulated as a multi-objective binary integer programming model in which skills, level of satisfaction and training cost of personnel are considered simultaneously in productive company. The purpose of this model is to obtain the best matching between candidates and positions. In this model, a set of methods such as a group analytic hierarchy process (GAHP, Shannon entropy, coefficient of variation (CV and fuzzy logic are used to calculate the weights of evaluation criteria, weights of position and coefficient of objective functions. This proposed model can rationalize the subjective judgments of decision makers with mathematic models.

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.

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.

Designing and optimising anaerobic digestion systems: A multi-objective non-linear goal programming approach

International Nuclear Information System (INIS)

Nixon, J.D.

2016-01-01

This paper presents a method for optimising the design parameters of an anaerobic digestion (AD) system by using first-order kinetics and multi-objective non-linear goal programming. A model is outlined that determines the ideal operating tank temperature and hydraulic retention time, based on objectives for minimising levelised cost of electricity, and maximising energy potential and feedstock mass reduction. The model is demonstrated for a continuously stirred tank reactor processing food waste in two case study locations. These locations are used to investigate the influence of different environmental and economic climates on optimal conditions. A sensitivity analysis is performed to further examine the variation in optimal results for different financial assumptions and objective weightings. The results identify the conditions for the preferred tank temperature to be in the psychrophilic, mesophilic or thermophilic range. For a tank temperature of 35 °C, ideal hydraulic retention times, in terms of achieving a minimum levelised electricity cost, were found to range from 29.9 to 33 days. Whilst there is a need for more detailed information on rate constants for use in first-order models, multi-objective optimisation modelling is considered to be a promising option for AD design. - Highlights: • Nonlinear goal programming is used to optimise anaerobic digestion systems. • Multiple objectives are set including minimising the levelised cost of electricity. • A model is developed and applied to case studies for the UK and India. • Optimal decisions are made for tank temperature and retention time. • A sensitivity analysis is carried out to investigate different model objectives.

Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints

Directory of Open Access Journals (Sweden)

Massimiliano Kaucic

2015-09-01

Full Text Available In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis.

Energy resource allocation using multi-objective goal programming: the case of Lebanon

International Nuclear Information System (INIS)

Mezher, T.; Chedid, R.; Zahabi, W.

1998-01-01

The traditional energy-resources allocation problem is concerned with the allocation of limited resources among the end-uses such that the overall return is maximized. In the past, several techniques have been used to deal with such a problem. In this paper, the energy allocation process is looked at from two points of view: economy and environment. The economic objectives include costs, efficiency, energy conservation, and employment generation. The environmental objectives consider environmental friendliness factors. The objective functions are first quantified and then transformed into mathematical language to obtain a multi-objective allocation model based upon pre-emptive goal programming techniques. The proposed method allows decision-makers to encourage or discourage specific energy resources for the various household end-uses. The case of Lebanon is examined to illustrate the usefulness of the proposed technique. (Copyright (c) 1998 Elsevier Science B.V., Amsterdam. All rights reserved.)

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.

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.

Optimizing the Regional Industrial Structure Based on the Environmental Carrying Capacity: An Inexact Fuzzy Multi-Objective Programming Model

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Wenyi Wang

2013-12-01

Full Text Available An inexact fuzzy multi-objective programming model (IFMOP based on the environmental carrying capacity is provided for industrial structure optimization problems. In the IFMOP model, both fuzzy linear programming (FLP and inexact linear programming (ILP methods are introduced into a multi-objective programming framework. It allows uncertainties to be directly communicated into the problem solving processing, and it can effectively reflect the complexity and uncertainty of an industrial system without impractical simplification. The two objective functions utilized in the optimization study are the maximum total output value and population size, and the constraints include water environmental capacity, water resource supply, atmospheric environmental capacity and energy supply. The model is subsequently employed in a realistic case for industrial development in the Tongzhou district, Beijing, China. The results demonstrate that the model can help to analyze whether the environmental carrying capacity of Tongzhou can meet the needs of the social economic objectives in the new town plan in the two scenarios and can assist decision makers in generating stable and balanced industrial structure patterns with consideration of the resources, energy and environmental constraints to meet the maximum social economic efficiency.

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.

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.

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.

A multi-objective possibilistic programming approach for locating distribution centers and allocating customers demands in supply chains

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Seyed Ahmad Yazdian

2011-01-01

Full Text Available In this paper, we present a multi-objective possibilistic programming model to locate distribution centers (DCs and allocate customers' demands in a supply chain network design (SCND problem. The SCND problem deals with determining locations of facilities (DCs and/or plants, and also shipment quantities between each two consecutive tier of the supply chain. The primary objective of this study is to consider different risk factors which are involved in both locating DCs and shipping products as an objective function. The risk consists of various components: the risks related to each potential DC location, the risk associated with each arc connecting a plant to a DC and the risk of shipment from a DC to a customer. The proposed method of this paper considers the risk phenomenon in fuzzy forms to handle the uncertainties inherent in these factors. A possibilistic programming approach is proposed to solve the resulted multi-objective problem and a numerical example for three levels of possibility is conducted to analyze the model.

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

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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.

Multi-Objective Fuzzy Linear Programming In Agricultural Production Planning

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H.M.I.U. Herath

2015-08-01

Full Text Available Abstract Modern agriculture is characterized by a series of conflicting optimization criteria that obstruct the decision-making process in the planning of agricultural production. Such criteria are usually net profit total cost total production etc. At the same time the decision making process in the agricultural production planning is often conducted with data that accidentally occur in nature or that are fuzzy not deterministic. Such data are the yields of various crops the prices of products and raw materials demand for the product the available quantities of production factors such as water labor etc. In this paper a fuzzy multi-criteria mathematical programming model is presented. This model is applied in a region of 10 districts in Sri Lanka where paddy is cultivated under irrigated and rain fed water in the two main seasons called Yala and Maha and the optimal production plan is achieved. This study was undertaken to find out the optimal allocation of land for paddy to get a better yield while satisfying the two conflicting objectives profit maximizing and cost minimizing subjected to the utilizing of water constraint and the demand constraint. Only the availability of land constraint is considered as a crisp in nature while objectives and other constraints are treated as fuzzy. It is observed that the MOFLP is an effective method to handle more than a single objective occurs in an uncertain vague environment.

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...

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.

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%.

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

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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.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

Science.gov (United States)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

Multi-Objective Optimization of Grillages Applying the Genetic Algorithm

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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

Analysis of the efficiency of the linearization techniques for solving multi-objective linear fractional programming problems by goal programming

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Tunjo Perić

2017-01-01

Full Text Available This paper presents and analyzes the applicability of three linearization techniques used for solving multi-objective linear fractional programming problems using the goal programming method. The three linearization techniques are: (1 Taylor’s polynomial linearization approximation, (2 the method of variable change, and (3 a modification of the method of variable change proposed in [20]. All three linearization techniques are presented and analyzed in two variants: (a using the optimal value of the objective functions as the decision makers’ aspirations, and (b the decision makers’ aspirations are given by the decision makers. As the criteria for the analysis we use the efficiency of the obtained solutions and the difficulties the analyst comes upon in preparing the linearization models. To analyze the applicability of the linearization techniques incorporated in the linear goal programming method we use an example of a financial structure optimization problem.

Role of exponential type random invexities for asymptotically sufficient efficiency conditions in semi-infinite multi-objective fractional programming.

Science.gov (United States)

Verma, Ram U; Seol, Youngsoo

2016-01-01

First a new notion of the random exponential Hanson-Antczak type [Formula: see text]-V-invexity is introduced, which generalizes most of the existing notions in the literature, second a random function [Formula: see text] of the second order is defined, and finally a class of asymptotically sufficient efficiency conditions in semi-infinite multi-objective fractional programming is established. Furthermore, several sets of asymptotic sufficiency results in which various generalized exponential type [Formula: see text]-V-invexity assumptions are imposed on certain vector functions whose components are the individual as well as some combinations of the problem functions are examined and proved. To the best of our knowledge, all the established results on the semi-infinite aspects of the multi-objective fractional programming are new, which is a significantly new emerging field of the interdisciplinary research in nature. We also observed that the investigated results can be modified and applied to several special classes of nonlinear programming problems.

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.

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.

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.

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.

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).

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

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.

Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

Science.gov (United States)

Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong

2014-03-01

A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

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.

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...

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

Science.gov (United States)

Zhong, Shuya; Pantelous, Athanasios A.; Beer, Michael; Zhou, Jian

2018-05-01

Offshore wind farm is an emerging source of renewable energy, which has been shown to have tremendous potential in recent years. In this blooming area, a key challenge is that the preventive maintenance of offshore turbines should be scheduled reasonably to satisfy the power supply without failure. In this direction, two significant goals should be considered simultaneously as a trade-off. One is to maximise the system reliability and the other is to minimise the maintenance related cost. Thus, a non-linear multi-objective programming model is proposed including two newly defined objectives with thirteen families of constraints suitable for the preventive maintenance of offshore wind farms. In order to solve our model effectively, the nondominated sorting genetic algorithm II, especially for the multi-objective optimisation is utilised and Pareto-optimal solutions of schedules can be obtained to offer adequate support to decision-makers. Finally, an example is given to illustrate the performances of the devised model and algorithm, and explore the relationships of the two targets with the help of a contrast model.

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)

Programming Scala Scalability = Functional Programming + Objects

CERN Document Server

Wampler, Dean

2009-01-01

Learn how to be more productive with Scala, a new multi-paradigm language for the Java Virtual Machine (JVM) that integrates features of both object-oriented and functional programming. With this book, you'll discover why Scala is ideal for highly scalable, component-based applications that support concurrency and distribution. Programming Scala clearly explains the advantages of Scala as a JVM language. You'll learn how to leverage the wealth of Java class libraries to meet the practical needs of enterprise and Internet projects more easily. Packed with code examples, this book provides us

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)

Multi-objective possibilistic model for portfolio selection with transaction cost

Science.gov (United States)

Jana, P.; Roy, T. K.; Mazumder, S. K.

2009-06-01

In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.

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)

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 ...

Genetic Algorithm (GA Method for Optimization of Multi-Reservoir Systems Operation

Directory of Open Access Journals (Sweden)

Shervin Momtahen

2006-01-01

Full Text Available A Genetic Algorithm (GA method for optimization of multi-reservoir systems operation is proposed in this paper. In this method, the parameters of operating policies are optimized using system simulation results. Hence, any operating problem with any sort of objective function, constraints and structure of operating policy can be optimized by GA. The method is applied to a 3-reservoir system and is compared with two traditional methods of Stochastic Dynamic Programming and Dynamic Programming and Regression. The results show that GA is superior both in objective function value and in computational speed. The proposed method is further improved using a mutation power updating rule and a varying period simulation method. The later is a novel procedure proposed in this paper that is believed to help in solving computational time problem in large systems. These revisions are evaluated and proved to be very useful in converging to better solutions in much less time. The final GA method is eventually evaluated as a very efficient procedure that is able to solve problems of large multi-reservoir system which is usually impossible by traditional methods. In fact, the real performance of the GA method starts where others fail to function.

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

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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.

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 ...

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

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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.

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.

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

An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems.

Science.gov (United States)

Yu, Hao; Solvang, Wei Deng

2016-05-31

Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment.

An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems

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Hao Yu

2016-05-01

Full Text Available Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment.

Multi-objective optimization of a series–parallel system using GPSIA

International Nuclear Information System (INIS)

Okafor, Ekene Gabriel; Sun Youchao

2012-01-01

The optimal solution of a multi-objective optimization problem (MOP) corresponds to a Pareto set that is characterized by a tradeoff between objectives. Genetic Pareto Set Identification Algorithm (GPSIA) proposed for reliability-redundant MOPs is a hybrid technique which combines genetic and heuristic principles to generate non-dominated solutions. Series–parallel system with active redundancy is studied in this paper. Reliability and cost were the research objective functions subject to cost and weight constraints. The results reveal an evenly distributed non-dominated front. The distances between successive Pareto points were used to evaluate the general performance of the method. Plots were also used to show the computational results for the type of system studied and the robustness of the technique is discussed in comparison with NSGA-II and SPEA-2.

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.

Systematic analysis of the heat exchanger arrangement problem using multi-objective genetic optimization

International Nuclear Information System (INIS)

Daróczy, László; Janiga, Gábor; Thévenin, Dominique

2014-01-01

A two-dimensional cross-flow tube bank heat exchanger arrangement problem with internal laminar flow is considered in this work. The objective is to optimize the arrangement of tubes and find the most favorable geometries, in order to simultaneously maximize the rate of heat exchange while obtaining a minimum pressure loss. A systematic study was performed involving a large number of simulations. The global optimization method NSGA-II was retained. A fully automatized in-house optimization environment was used to solve the problem, including mesh generation and CFD (computational fluid dynamics) simulations. The optimization was performed in parallel on a Linux cluster with a very good speed-up. The main purpose of this article is to illustrate and analyze a heat exchanger arrangement problem in its most general form and to provide a fundamental understanding of the structure of the Pareto front and optimal geometries. The considered conditions are particularly suited for low-power applications, as found in a growing number of practical systems in an effort toward increasing energy efficiency. For such a detailed analysis with more than 140 000 CFD-based evaluations, a design-of-experiment study involving a response surface would not be sufficient. Instead, all evaluations rely on a direct solution using a CFD solver. - Highlights: • Cross-flow tube bank heat exchanger arrangement problem. • A fully automatized multi-objective optimization based on genetic algorithm. • A systematic study involving a large number of CFD (computational fluid dynamics) simulations

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.

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.

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

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

Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option

International Nuclear Information System (INIS)

Tabar, Vahid Sohrabi; Jirdehi, Mehdi Ahmadi; Hemmati, Reza

2017-01-01

Renewable energy resources are often known as cost-effective and lucrative resources and have been widely developed due to environmental-economic issues. Renewable energy utilization even in small scale (e.g., microgrid networks) has attracted significant attention. Energy management in microgrid can be carried out based on the generating side management or demand side management. In this paper, portable renewable energy resource are modeled and included in microgrid energy management as a demand response option. Utilizing such resources could supply the load when microgrid cannot serve the demand. This paper addresses energy management and scheduling in microgrid including thermal and electrical loads, renewable energy sources (solar and wind), CHP, conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical ones), and portable renewable energy resource (PRER). Operational cost of microgrid and air pollution are considered as objective functions. Uncertainties related to the parameters are incorporated to make a stochastic programming. The proposed problem is expressed as a constrained, multi-objective, linear, and mixed-integer programing. Augmented Epsilon-constraint method is used to solve the problem. Final results and calculations are achieved using GAMS24.1.3/CPLEX12.5.1. Simulation results demonstrate the viability and effectiveness of the proposed method in microgrid energy management. - Highlights: • Introducing portable renewable energy resource (PRER) and considering effect of them. • Considering reserve margin and sensitivity analysis for validate robustness. • Multi objective and stochastic management with considering various loads and sources. • Using augmented Epsilon-constraint method to solve multi objective program. • Highly decreasing total cost and pollution with PRER in stochastic state.

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.

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)

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)

An overview of solution methods for multi-objective mixed integer linear programming programs

DEFF Research Database (Denmark)

Andersen, Kim Allan; Stidsen, Thomas Riis

Multiple objective mixed integer linear programming (MOMIP) problems are notoriously hard to solve to optimality, i.e. finding the complete set of non-dominated solutions. We will give an overview of existing methods. Among those are interactive methods, the two phases method and enumeration...... methods. In particular we will discuss the existing branch and bound approaches for solving multiple objective integer programming problems. Despite the fact that branch and bound methods has been applied successfully to integer programming problems with one criterion only a few attempts has been made...

An integrated approach to engineering curricula improvement with multi-objective decision modeling and linear programming

Science.gov (United States)

Shea, John E.

from a catalog of courses is difficult because of the many factors being considered. To assist this process, the multi-objective model and the curriculum requirements were incorporated in a linear program to select the "optimum" curriculum. The application of this tool was also beneficial in identifying the active constraints that limit curriculum development and content.

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...

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...

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

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

Exergoeconomic multi objective optimization and sensitivity analysis of a regenerative Brayton cycle

International Nuclear Information System (INIS)

Naserian, Mohammad Mahdi; Farahat, Said; Sarhaddi, Faramarz

2016-01-01

Highlights: • Finite time exergoeconomic multi objective optimization of a Brayton cycle. • Comparing the exergoeconomic and the ecological function optimization results. • Inserting the cost of fluid streams concept into finite-time thermodynamics. • Exergoeconomic sensitivity analysis of a regenerative Brayton cycle. • Suggesting the cycle performance curve drawing and utilization. - Abstract: In this study, the optimal performance of a regenerative Brayton cycle is sought through power maximization and then exergoeconomic optimization using finite-time thermodynamic concept and finite-size components. Optimizations are performed using genetic algorithm. In order to take into account the finite-time and finite-size concepts in current problem, a dimensionless mass-flow parameter is used deploying time variations. The decision variables for the optimum state (of multi objective exergoeconomic optimization) are compared to the maximum power state. One can see that the multi objective exergoeconomic optimization results in a better performance than that obtained with the maximum power state. The results demonstrate that system performance at optimum point of multi objective optimization yields 71% of the maximum power, but only with exergy destruction as 24% of the amount that is produced at the maximum power state and 67% lower total cost rate than that of the maximum power state. In order to assess the impact of the variation of the decision variables on the objective functions, sensitivity analysis is conducted. Finally, the cycle performance curve drawing according to exergoeconomic multi objective optimization results and its utilization, are suggested.

Block level energy planning for domestic lighting - a multi-objective fuzzy linear programming approach

Energy Technology Data Exchange (ETDEWEB)

Jana, C. [Indian Inst. of Social Welfare and Business Management, Kolkata (India); Chattopadhyay, R.N. [Indian Inst. of Technology, Kharagpur (India). Rural Development Centre

2004-09-01

Creating provisions for domestic lighting is important for rural development. Its significance in rural economy is unquestionable since some activities, like literacy, education and manufacture of craft items and other cottage products are largely dependent on domestic lighting facilities for their progress and prosperity. Thus, in rural energy planning, domestic lighting remains a key sector for allocation of investments. For rational allocation, decision makers need alternative strategies for identifying adequate and proper investment structure corresponding to appropriate sources and precise devices. The present study aims at designing a model of energy utilisation by developing a decision support frame for an optimised solution to the problem, taking into consideration four sources and six devices suitable for the study area, namely Narayangarh Block of Midnapore District in India. Since the data available from rural and unorganised sectors are often ill-defined and subjective in nature, many coefficients are fuzzy numbers, and hence several constraints appear to be fuzzy expressions. In this study, the energy allocation model is initiated with three separate objectives for optimisation, namely minimising the total cost, minimising the use of non-local sources of energy and maximising the overall efficiency of the system. Since each of the above objective-based solutions has relevance to the needs of the society and economy, it is necessary to build a model that makes a compromise among the three individual solutions. This multi-objective fuzzy linear programming (MOFLP) model, solved in a compromising decision support frame, seems to be a more rational alternative than single objective linear programming model in rural energy planning. (author)

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...

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.

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.

Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

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Yan Sun

2015-09-01

Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

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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.

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.

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.

OPTIMASI POLA TANAM PADA LAHAN KERING DI KOTA PEKANBARU DENGAN MENGGUNAKAN METODA MULTI OBJECTIVE (GOAL PROGRAMMING

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Vera Devani

2012-12-01

Full Text Available Salah satu alternatif pilihan yang diharapkan dapat meningkatkan potensi produksi tanaman dalam rangka memenuhi kebutuhan pangan adalah pendayagunaan lahan kering. Pada model Linear Programming kendala-kendala fungsional menjadi pembatas bagi usaha memaksimumkan atau meminimumkan fungsi tujuan, maka pada Multi Objective Goal Programming kendala-kendala itu merupakan sarana untuk mewujudkan sasaran yang hendak dicapai. Penelitian ini bertujuan untuk menentukan pola tanam sayur-sayuran, menentukan kendala sasaran yang dapat dicapai, dan mengetahui sensitivitas terhadap solusi optimum yang telah dicapai. Dari penelitian diperoleh bahwa dengan luas lahan 504 Ha, jumlah tenaga kerja 300 orang, kebutuhan pupuk kandang sebanyak 15.000 kg/Ha, dan kebutuhan pupuk urea sebanyak 350 kg/Ha dapat mengoptimasi pola tanam dengan menanam jenis komoditas sayuran berupa ketimun dan sawi.

Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposaL

Energy Technology Data Exchange (ETDEWEB)

Wichapa, Narong; Khokhajaikiat, Porntep

2017-07-01

Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposaL

International Nuclear Information System (INIS)

Wichapa, Narong; Khokhajaikiat, Porntep

2017-01-01

Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

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 ...

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.

Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

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Dan Wu

2009-06-01

Full Text Available The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

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.

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

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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.

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)

Digital fabrication of multi-material biomedical objects

Energy Technology Data Exchange (ETDEWEB)

Cheung, H H; Choi, S H, E-mail: shchoi@hku.h [Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road (Hong Kong)

2009-12-15

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module and a virtual reality (VR) simulation module. The DMMVP module is used to model discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multi-material (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multi-material objects, which can be subsequently visualized and analysed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a DMM human spine and an FGM intervertebral disc spacer are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show that the MMVP system is a practical tool for modelling, visualization, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. The system may be adapted to control MMLM machines with appropriate hardware for physical fabrication of biomedical objects.

Digital fabrication of multi-material biomedical objects

International Nuclear Information System (INIS)

Cheung, H H; Choi, S H

2009-01-01

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module and a virtual reality (VR) simulation module. The DMMVP module is used to model discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multi-material (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multi-material objects, which can be subsequently visualized and analysed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a DMM human spine and an FGM intervertebral disc spacer are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show that the MMVP system is a practical tool for modelling, visualization, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. The system may be adapted to control MMLM machines with appropriate hardware for physical fabrication of biomedical objects.

Farm Planning by Fuzzy Multi Objective Programming Model

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m Raei Jadidi

2010-05-01

Full Text Available In current study, Fuzzy Goal Programming (FGP model by considering a set of social and economic goals, was applied to optimal land allocation in Koshksaray district, Marand city, East Azarbaijan province, Iran. Farmer goals including total cultivable area, factor of production, production levels of various crops and total expected profit were considered fuzzily in establishment of the model. The goals were considered by 16 scenarios in the form of single objective, compound and priority structures. Results showed that, cost minimization in single objective and compound scenario is the best as compared with current conditions. In priority structure, scenario 10 with priorities of profit maximization, cost minimization, satisfying of production goals considering cost minimization and production goals, and scenario 13 with priorities of profit maximization, satisfying factor of production goals, cost minimization and fulfilling production goals, had minimum Euclidean Distance and satisfied the fuzzy objectives. Moreover, dry barley, irrigated and dry wheat and irrigated barely had maximum and minimum cultivated area, respectively. According to the findings, by reallocation of resources, farmers can achieve their better goals and objectives.

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

Effect of objective function on multi-objective inverse planning of radiation therapy

International Nuclear Information System (INIS)

Li Guoli; Wu Yican; Song Gang; Wang Shifang

2006-01-01

There are two kinds of objective functions in radiotherapy inverse planning: dose distribution-based and Dose-Volume Histogram (DVH)-based functions. The treatment planning in our days is still a trial and error process because the multi-objective problem is solved by transforming it into a single objective problem using a specific set of weights for each object. This work investigates the problem of objective function setting based on Pareto multi-optimization theory, and compares the effect on multi-objective inverse planning of those two kinds of objective functions including calculation time, converge speed, etc. The basis of objective function setting on inverse planning is discussed. (authors)

Multi-objective optimization of a joule cycle for re-liquefaction of the Liquefied Natural Gas

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Babaelahi, M.

2011-01-01

Highlights: → A typical LNG boil off gas re-liquefaction plant system is optimized. → Objective functions based on thermodynamic and thermoeconomic analysis are obtained. → The cost of the system product and the exergetic efficiency are optimized, simultaneously. → A decision-making process for selection of the final optimal design is introduced. → Results obtained using various optimization scenarios are compared and discussed. - Abstract: A LNG re-liquefaction plant is optimized with a multi-objective approach which simultaneously considers exergetic and exergoeconomic objectives. In this regard, optimization is performed in order to maximize the exergetic efficiency of plant and minimize the unit cost of the system product (refrigeration effect), simultaneously. Thermodynamic modeling is performed based on energy and exergy analyses, while an exergoeconomic model based on the total revenue requirement (TRR) are developed. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms namely NSGA-II. This approach which is based on the Genetic Algorithm is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained and a final optimal solution is selected in a decision-making process. An example of decision-making process for selection of the final solution from the available optimal points of the Pareto frontier is presented here. The feature of selected final optimal system is compared with corresponding features of the base case and exergoeconomic single-objective optimized systems and discussed.

Multi-Objective Scheduling Optimization Based on a Modified Non-Dominated Sorting Genetic Algorithm-II in Voltage Source Converter−Multi-Terminal High Voltage DC Grid-Connected Offshore Wind Farms with Battery Energy Storage Systems

Directory of Open Access Journals (Sweden)

Ho-Young Kim

2017-07-01

Full Text Available Improving the performance of power systems has become a challenging task for system operators in an open access environment. This paper presents an optimization approach for solving the multi-objective scheduling problem using a modified non-dominated sorting genetic algorithm in a hybrid network of meshed alternating current (AC/wind farm grids. This approach considers voltage and power control modes based on multi-terminal voltage source converter high-voltage direct current (MTDC and battery energy storage systems (BESS. To enhance the hybrid network station performance, we implement an optimal process based on the battery energy storage system operational strategy for multi-objective scheduling over a 24 h demand profile. Furthermore, the proposed approach is formulated as a master problem and a set of sub-problems associated with the hybrid network station to improve the overall computational efficiency using Benders’ decomposition. Based on the results of the simulations conducted on modified institute of electrical and electronics engineers (IEEE-14 bus and IEEE-118 bus test systems, we demonstrate and confirm the applicability, effectiveness and validity of the proposed approach.

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.

Solving a bi-objective mathematical programming model for bloodmobiles location routing problem

Directory of Open Access Journals (Sweden)

Masoud Rabbani

2017-01-01

Full Text Available Perishability of platelets, uncertainty of donors’ arrival and conflicting views in platelet supply chain have made platelet supply chain planning a problematic issue. In this paper, mobile blood collection system for platelet production is investigated. Two mathematical models are presented to cover the bloodmobile collection planning problem. The first model is a multi-objective fuzzy mathematical programming in which the bloodmobiles locations are considered with the aim of maximizing potential amount of blood collection and minimizing the operational cost. The second model is a vehicle routing problem with time windows which studies the shuttles routing problem. To tackle the first model, it is reformulated as a crisp multi objective linear programming model and then solved through a fuzzy multi objective programming approach. Several sensitivity analysis are conducted on important parameters to demonstrate the applicability of the proposed model. The proposed model is then solved by using a tailored Simulated Annealing (SA algorithm. The numerical results demonstrate promising efficiency of the proposed solution method.

A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem

Science.gov (United States)

Moradgholi, Mostafa; Paydar, Mohammad Mahdi; Mahdavi, Iraj; Jouzdani, Javid

2016-09-01

Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The bi-objective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method.

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.

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty.

Science.gov (United States)

Chen, Xudong; Xu, Zhongwen; Yao, Liming; Ma, Ning

2018-03-05

This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

Multi-objective optimization of Stirling engine using Finite Physical Dimensions Thermodynamics (FPDT) method

International Nuclear Information System (INIS)

Li, Ruijie; Grosu, Lavinia; Queiros-Conde, Diogo

2016-01-01

Highlights: • A gamma Stirling engine has been optimized using FPDT method by multi-objective criteria. • Genetic algorithm and decision making methods were used to get Pareto frontier and optimum points. • It shows: total thermal conductance, hot temperature, stroke and diameter ratios can be improved. - Abstract: In this paper, a solar energy powered gamma type SE has been optimized using Finite Physical Dimensions Thermodynamics (FPDT) method by multi-objective criteria. Genetic algorithm was used to get the Pareto frontier, and optimum points were obtained using the decision making methods of LINMAP and TOPSIS. The optimization results have been compared with those obtained using the ecological method. It was shown that the multi-objective optimization in this paper has a better balance among the optimizing criteria (maximum mechanical power, maximum thermal efficiency and minimum entropy generation flow). The effects of the hot source temperature and the total thermal conductance of the engine on the Pareto frontier have been also studied. This sensibility study shows that an increase in the hot reservoir temperature can increase the output mechanical power, the thermal efficiency of the engine, but also the entropy generation rate. In addition to this, an increase of the total thermal conductance of the engine can strongly increase the output mechanical power and only slightly increase the thermal efficiency. These results allow us to improve the engine performance after some modifications as geometrical dimensions (diameter, stroke, heat exchange surface, etc.) and physical parameters (temperature, thermal conductivity).

Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs

International Nuclear Information System (INIS)

Nwulu, Nnamdi I.; Xia, Xiaohua

2015-01-01

Highlights: • In this work, a game theory based DR program is integrated into the DEED problem. • Objectives are to minimize fuel and emissions costs and maximize the DR benefit. • Optimal generator output, customer load and customer incentive are determined. • Developed model is tested with two different scenarios. • Model provides superior results than independent optimization of DR or DEED. - Abstract: The dynamic economic emission dispatch (DEED) of electric power generation is a multi-objective mathematical optimization problem with two objective functions. The first objective is to minimize all the fuel costs of the generators in the power system, whilst the second objective seeks to minimize the emissions cost. Both objective functions are subject to constraints such as load demand constraint, ramp rate constraint, amongst other constraints. In this work, we integrate a game theory based demand response program into the DEED problem. The game theory based demand response program determines the optimal hourly incentive to be offered to customers who sign up for load curtailment. The game theory model has in built mechanisms to ensure that the incentive offered the customers is greater than the cost of interruption while simultaneously being beneficial to the utility. The combined DEED and game theoretic demand response model presented in this work, minimizes fuel and emissions costs and simultaneously determines the optimal incentive and load curtailment customers have to perform for maximal power system relief. The developed model is tested on two test systems with industrial customers and obtained results indicate the practical benefits of the proposed model

Programming paradigms in an object-oriented multi-media standard

NARCIS (Netherlands)

D.J. Duke; I. Herman (Ivan)

1997-01-01

textabstractOf the various programming paradigms in use today, object-orientation is probably the most successful in terms of industrial take-up and application, particularly in the field of multimedia. It is therefore unsurprising that this technology has been adopted by ISO/IEC JTC1/SC24 as the

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

Enhancing community based health programs in Iran: a multi-objective location-allocation model.

Science.gov (United States)

Khodaparasti, S; Maleki, H R; Jahedi, S; Bruni, M E; Beraldi, P

2017-12-01

Community Based Organizations (CBOs) are important health system stakeholders with the mission of addressing the social and economic needs of individuals and groups in a defined geographic area, usually no larger than a county. The access and success efforts of CBOs vary, depending on the integration between health care providers and CBOs but also in relation to the community participation level. To achieve widespread results, it is important to carefully design an efficient network which can serve as a bridge between the community and the health care system. This study addresses this challenge through a location-allocation model that deals with the hierarchical nature of the system explicitly. To reflect social welfare concerns of equity, local accessibility, and efficiency, we develop the model in a multi-objective framework, capturing the ambiguity in the decision makers' aspiration levels through a fuzzy goal programming approach. This study reports the findings for the real case of Shiraz city, Fars province, Iran, obtained by a thorough analysis of the results.

A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization

Czech Academy of Sciences Publication Activity Database

Hájek, J.; Szöllös, A.; Šístek, Jakub

2010-01-01

Roč. 41, 7-8 (2010), s. 1031-1057 ISSN 0965-9978 R&D Projects: GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z10190503 Keywords : multi-objective optimization * micro-genetic algorithm * diversity * Pareto archive Subject RIV: BA - General Mathematics Impact factor: 1.004, year: 2010 http://www.sciencedirect.com/science/article/pii/S0965997810000451

A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization

Czech Academy of Sciences Publication Activity Database

Hájek, J.; Szöllös, A.; Šístek, Jakub

2010-01-01

Roč. 41, 7-8 (2010), s. 1031-1057 ISSN 0965-9978 R&D Projects: GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z10190503 Keywords : multi-objective optimization * micro- genetic algorithm * diversity * Pareto archive Subject RIV: BA - General Mathematics Impact factor: 1.004, year: 2010 http://www.sciencedirect.com/science/article/pii/S0965997810000451

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

Science.gov (United States)

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

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.

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.

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.

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty

Directory of Open Access Journals (Sweden)

Xudong Chen

2018-03-01

Full Text Available This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

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.

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.

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.

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Directory of Open Access Journals (Sweden)

Samira Bolouri

2018-01-01

Full Text Available Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000 to stations in a discrete space in a GIS, assuming that the stations’ capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

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.

An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation

International Nuclear Information System (INIS)

Niknam, Taher; Azizipanah-Abarghooee, Rasoul; Narimani, Mohammad Rasoul

2012-01-01

Highlights: ► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Present an improved multi-objective teaching–learning-based optimization. -- Abstract: This paper proposes a stochastic model for optimal energy management with the goal of cost and emission minimization. In this model, the uncertainties related to the forecasted values for load demand, available output power of wind and photovoltaic units and market price are modeled by a scenario-based stochastic programming. In the presented method, scenarios are generated by a roulette wheel mechanism based on probability distribution functions of the input random variables. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front. In the proposed stochastic optimization method, a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm. Also, a set of non-dominated solutions are stored in a repository during the simulation process. Meanwhile, the size of the repository is controlled by usage of a fuzzy-based clustering technique. The best expected compromise solution stored in the repository is selected via the niching mechanism in a way that solutions are encouraged to seek the lesser explored regions. The proposed framework is applied in a typical grid-connected micro grid in order to verify its efficiency and feasibility.

visPIG--a web tool for producing multi-region, multi-track, multi-scale plots of genetic data.

Directory of Open Access Journals (Sweden)

Matthew Scales

Full Text Available We present VISual Plotting Interface for Genetics (visPIG; http://vispig.icr.ac.uk, a web application to produce multi-track, multi-scale, multi-region plots of genetic data. visPIG has been designed to allow users not well versed with mathematical software packages and/or programming languages such as R, Matlab®, Python, etc., to integrate data from multiple sources for interpretation and to easily create publication-ready figures. While web tools such as the UCSC Genome Browser or the WashU Epigenome Browser allow custom data uploads, such tools are primarily designed for data exploration. This is also true for the desktop-run Integrative Genomics Viewer (IGV. Other locally run data visualisation software such as Circos require significant computer skills of the user. The visPIG web application is a menu-based interface that allows users to upload custom data tracks and set track-specific parameters. Figures can be downloaded as PDF or PNG files. For sensitive data, the underlying R code can also be downloaded and run locally. visPIG is multi-track: it can display many different data types (e.g association, functional annotation, intensity, interaction, heat map data,…. It also allows annotation of genes and other custom features in the plotted region(s. Data tracks can be plotted individually or on a single figure. visPIG is multi-region: it supports plotting multiple regions, be they kilo- or megabases apart or even on different chromosomes. Finally, visPIG is multi-scale: a sub-region of particular interest can be 'zoomed' in. We describe the various features of visPIG and illustrate its utility with examples. visPIG is freely available through http://vispig.icr.ac.uk under a GNU General Public License (GPLv3.

Multi area and multistage expansion-planning of electricity supply with sustainable energy development criteria: a multi objective model

Energy Technology Data Exchange (ETDEWEB)

Unsihuay-Vila, Clodomiro; Marangon-Lima, J.W.; Souza, A.C Zambroni de [Universidade Federal de Itajuba (UNIFEI), MG (Brazil)], emails: clodomirounsihuayvila @gmail.com, marangon@unifei.edu.br, zambroni@unifei.edu.br; Perez-Arriaga, I.J. [Universidad Pontificia Comillas, Madrid (Spain)], email: ipa@mit.edu

2010-07-01

A novel multi objective, multi area and multistage model to long-term expansion-planning of integrated generation and transmission corridors incorporating sustainable energy developing is presented in this paper. The proposed MESEDES model is a multi-regional multi-objective and 'bottom-up' energy model which considers the electricity generation/transmission value-chain, i.e., power generation alternatives including renewable, nuclear and traditional thermal generation along with transmission corridors. The model decides the optimal location and timing of the electricity generation/transmission abroad the multistage planning horizon. The MESEDES model considers three objectives belonging to sustainable energy development criteria such as: a) the minimization of investments and operation costs of : power generation, transmission corridors, energy efficiency (demand side management (DSM) programs) considering CO2 capture technologies; b) minimization of Life Cycle Greenhouse Gas Emissions (LC GHG); c) maximization of the diversification of electricity generation mix. The proposed model consider aspects of the carbon abatement policy under the CDM - Clean Development Mechanism or European Union Greenhouse Gas Emission Trading Scheme. A case study is used to illustrate the proposed framework. (author)

Presenting a Multi Objective Model for Supplier Selection in Order to Reduce Green House Gas Emission under Uncertion Demand

Directory of Open Access Journals (Sweden)

Habibollah Mohamadi

2014-08-01

Full Text Available Recently, much attention has been given to Stochastic demand due to uncertainty in the real -world. In the literature, decision-making models and suppliers' selection do not often consider inventory management as part of shopping problems. On the other hand, the environmental sustainability of a supply chain depends on the shopping strategy of the supply chain members. The supplier selection plays an important role in the green chain. In this paper, a multi-objective nonlinear integer programming model for selecting a set of supplier considering Stochastic demand is proposed. while the cost of purchasing include the total cost, holding and stock out costs, rejected units, units have been delivered sooner, and total green house gas emissions are minimized, while the obtained total score from the supplier assessment process is maximized. It is assumed, the purchaser provides the different products from the number predetermined supplier to a with Stochastic demand and the uniform probability distribution function. The product price depends on the order quantity for each product line is intended. Multi-objective models using known methods, such as Lp-metric has become an objective function and then uses genetic algorithms and simulated annealing meta-heuristic is solved.

Point efficiency of the notion in multi objective programming

International Nuclear Information System (INIS)

Kampempe, B.J.D.; Manya, N.L.

2010-01-01

The approaches to the problem of multi-objective linear programming stochastic (PLMS) which have been proposed so far in the literature are not really satisfactory (9,11), so we want, in this article, to approach the problem of PLMS using the concept of efficiency point. It is also necessary to define what is meant by efficiency point in the context of PLMS. This is precisely the purpose of this article. In fact, it seeks to provide specific definitions of effective solutions that are not only mathematically consistent, but also have significance to a decision maker faced with such a decision problem. As a result, we have to use the concept of dominance in the time of PLMS, in the context where one has ordinal preference but no utility functions. In this paper, we propose to further explore the concepts of dominance and efficiency point. Indeed, the whole point P effective solutions are usually very broad and as we shall see, it can be identical to X. Accordingly, we will try to relax the definition of dominance relation >p in order to obtain other types of dominance point less demanding and generating subsets may be more effective especially interesting for a decision maker. We shall have to distinguish two other families of dominance relations point : the dominance and dominance scenario test, and within sets of efficient solutions proposed by these last two relations, we will focus on subsets of efficient solutions called sponsored and unanimous. We will study the properties of these various relationships and the possible links between the different effective resulting sets in order to find them and to calculate them explicitly. Finally we will establish some connections between different notions of efficiency and timely concept of Pareto-efficient solution on the deterministic case (PLMD)

Multi-Objective Nonlinear Model Predictive Control: Lexicographic Method

OpenAIRE

Zheng, Tao; Wu, Gang; Liu, Guang-Hong; Ling, Qing

2010-01-01

In this chapter, to avoid the disadvantages of weight coefficients in multi-objective dynamic optimization, lexicographic (completely stratified) and partially stratified frameworks of multi-objective controller are proposed. The lexicographic framework is absolutely prioritydriven and the partially stratified framework is a modification of it, they both can solve the multi-objective control problem with the concept of priority for objective’s relative importance, while the latter one is mo...

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.

An export coefficient based inexact fuzzy bi-level multi-objective programming model for the management of agricultural nonpoint source pollution under uncertainty

Science.gov (United States)

Cai, Yanpeng; Rong, Qiangqiang; Yang, Zhifeng; Yue, Wencong; Tan, Qian

2018-02-01

In this research, an export coefficient based inexact fuzzy bi-level multi-objective programming (EC-IFBLMOP) model was developed through integrating export coefficient model (ECM), interval parameter programming (IPP) and fuzzy parameter programming (FPP) within a bi-level multi-objective programming framework. The proposed EC-IFBLMOP model can effectively deal with the multiple uncertainties expressed as discrete intervals and fuzzy membership functions. Also, the complexities in agricultural systems, such as the cooperation and gaming relationship between the decision makers at different levels, can be fully considered in the model. The developed model was then applied to identify the optimal land use patterns and BMP implementing levels for agricultural nonpoint source (NPS) pollution management in a subcatchment in the upper stream watershed of the Miyun Reservoir in north China. The results of the model showed that the desired optimal land use patterns and implementing levels of best management of practices (BMPs) would be obtained. It is the gaming result between the upper- and lower-level decision makers, when the allowable discharge amounts of NPS pollutants were limited. Moreover, results corresponding to different decision scenarios could provide a set of decision alternatives for the upper- and lower-level decision makers to identify the most appropriate management strategy. The model has a good applicability and can be effectively utilized for agricultural NPS pollution management.

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.

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.

Multi-objective Transmission Planning Paper

DEFF Research Database (Denmark)

Xu, Zhao; Dong, Zhao Yang; Wong, Kit Po

2009-01-01

This paper describes a transmission expansion planning method based on multi-objective optimization (MOOP). The method starts with constructing a candidate pool of feasible expansion plans, followed by selection of the best candidates through MOOP, of which multiple objectives are tackled...

A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

Science.gov (United States)

Moghaddam, Kamran S.; Usher, John S.

2011-07-01

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

Performance improvement of developed program by using multi-thread technique

Directory of Open Access Journals (Sweden)

Surasak Jabal

2015-03-01

Full Text Available This research presented how to use a multi-thread programming technique to improve the performance of a program written by Windows Presentation Foundation (WPF. The Computer Assisted Instruction (CAI software, named GAME24, was selected to use as a case study. This study composed of two main parts. The first part was about design and modification of the program structure upon the Object Oriented Programing (OOP approach. The second part was about coding the program using the multi-thread technique which the number of threads were based on the calculated Catalan number. The result showed that the multi-thread programming technique increased the performance of the program 44%-88% compared to the single-thread technique. In addition, it has been found that the number of cores in the CPU also increase the performance of multithreaded program proportionally.

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

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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.

Multi-Objective Optimization Control for the Aerospace Dual-Active Bridge Power Converter

Directory of Open Access Journals (Sweden)

Tao Lei

2018-05-01

Full Text Available With the development of More Electrical Aircraft (MEA, the electrification of secondary power systems in aircraft is becoming more and more common. As the key power conversion device, the dual active bridge (DAB converter is the power interface for the energy storage system with the high voltage direct current (HVDC bus in aircraft electrical power systems. In this paper, a DAB DC-DC converter is designed to meet aviation requirements. The extended dual phase shifted control strategy is adopted, and a multi-objective genetic algorithm is applied to optimize its operating performance. Considering the three indicators of inductance current root mean square root (RMS value, negative reverse power and direct current (DC bias component of the current for the high frequency transformer as the optimization objectives, the DAB converter’s optimization model is derived to achieve soft switching as the main constraint condition. Optimized methods of controlling quantity for the DAB based on the evolution and genetic algorithm is used to solve the model, and a number of optimal control parameters are obtained under different load conditions. The results of digital, hard-in-loop simulation and hardware prototype experiments show that the three performance indexes are all suppressed greatly, and the optimization method proposed in this paper is reasonable. The work of this paper provides a theoretical basis and researching method for the multi-objective optimization of the power converter in the aircraft electrical power system.

Multi-objective group scheduling optimization integrated with preventive maintenance

Science.gov (United States)

Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

2017-11-01

This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

Science.gov (United States)

Qu, Shaojian; Ji, Ying

2016-01-01

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

A fuzzy multi-objective optimization model for sustainable reverse logistics network design

DEFF Research Database (Denmark)

Govindan, Kannan; Paam, Parichehr; Abtahi, Amir Reza

2016-01-01

Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider...... a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order...... these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design...

Multi-Objective Sensitivity Analyses for Power Generation Mix: Malaysia Case Study

OpenAIRE

Siti Mariam Mohd Shokri; Nofri Yenita Dahlan; Hasmaini Mohamad

2017-01-01

This paper presents an optimization framework to determine long-term optimal generation mix for Malaysia Power Sector using Dynamic Programming (DP) technique. Several new candidate units with a pre-defined MW capacity were included in the model for generation expansion planning from coal, natural gas, hydro and renewable energy (RE). Four objective cases were considered, 1) economic cost, 2) environmental, 3) reliability and 4) multi-objectives that combining the three cases. Results show th...

Genetic algorithms and genetic programming for multiscale modeling: Applications in materials science and chemistry and advances in scalability

Science.gov (United States)

Sastry, Kumara Narasimha

2007-03-01

Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common

A multi-objective approach to solid waste management

International Nuclear Information System (INIS)

Galante, Giacomo; Aiello, Giuseppe; Enea, Mario; Panascia, Enrico

2010-01-01

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy).

A multi-objective approach to solid waste management.

Science.gov (United States)

Galante, Giacomo; Aiello, Giuseppe; Enea, Mario; Panascia, Enrico

2010-01-01

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy). 2010 Elsevier Ltd. All rights reserved.

Multi Camera Multi Object Tracking using Block Search over Epipolar Geometry

Directory of Open Access Journals (Sweden)

Saman Sargolzaei

2000-01-01

Full Text Available We present strategy for multi-objects tracking in multi camera environment for the surveillance and security application where tracking multitude subjects are of utmost importance in a crowded scene. Our technique assumes partially overlapped multi-camera setup where cameras share common view from different angle to assess positions and activities of subjects under suspicion. To establish spatial correspondence between camera views we employ an epipolar geometry technique. We propose an overlapped block search method to find the interested pattern (target in new frames. Color pattern update scheme has been considered to further optimize the efficiency of the object tracking when object pattern changes due to object motion in the field of views of the cameras. Evaluation of our approach is presented with the results on PETS2007 dataset..

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.

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.

A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

Science.gov (United States)

Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

2015-06-01

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

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...

Designing a Web Spam Classifier Based on Feature Fusion in the Layered Multi-Population Genetic Programming Framework

Directory of Open Access Journals (Sweden)

Amir Hosein KEYHANIPOUR

2013-11-01

Full Text Available Nowadays, Web spam pages are a critical challenge for Web retrieval systems which have drastic influence on the performance of such systems. Although these systems try to combat the impact of spam pages on their final results list, spammers increasingly use more sophisticated techniques to increase the number of views for their intended pages in order to have more commercial success. This paper employs the recently proposed Layered Multi-population Genetic Programming model for Web spam detection task as well application of correlation coefficient analysis for feature space reduction. Based on our tentative results, the designed classifier, which is based on a combination of easy to compute features, has a very reasonable performance in comparison with similar methods.

Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposal

Directory of Open Access Journals (Sweden)

Narong Wichapa

2017-11-01

Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

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

Thermo-economic and environmental analyses based multi-objective optimization of vapor compression–absorption cascaded refrigeration system using NSGA-II technique

International Nuclear Information System (INIS)

Jain, Vaibhav; Sachdeva, Gulshan; Kachhwaha, Surendra Singh; Patel, Bhavesh

2016-01-01

Highlights: • It addresses multi-objective optimization study on cascaded refrigeration system. • Cascaded system is a promising decarburizing and energy efficient technology. • NSGA-II technique is used for multi-objective optimization. • Total annual product cost and irreversibility rate are simultaneously optimized. - Abstract: Present work optimizes the performance of 170 kW vapor compression–absorption cascaded refrigeration system (VCACRS) based on combined thermodynamic, economic and environmental parameters using Non-dominated Sort Genetic Algorithm-II (NSGA-II) technique. Two objective functions including the total irreversibility rate (as a thermodynamic criterion) and the total product cost (as an economic criterion) of the system are considered simultaneously for multi-objective optimization of VCACRS. The capital and maintenance costs of the system components, the operational cost, and the penalty cost due to CO_2 emission are included in the total product cost of the system. Three optimized systems including a single-objective thermodynamic optimized, a single-objective economic optimized and a multi-objective optimized are analyzed and compared. The results showed that the multi-objective design considers the combined thermodynamic and total product cost criteria better than the two individual single-objective thermodynamic and total product cost optimized designs.

Benchmarking and performance enhancement framework for multi-staging object-oriented languages

Directory of Open Access Journals (Sweden)

Ahmed H. Yousef

2013-06-01

Full Text Available This paper focuses on verifying the readiness, feasibility, generality and usefulness of multi-staging programming in software applications. We present a benchmark designed to evaluate the performance gain of different multi-staging programming (MSP languages implementations of object oriented languages. The benchmarks in this suite cover different tests that range from classic simple examples (like matrix algebra to advanced examples (like encryption and image processing. The benchmark is applied to compare the performance gain of two different MSP implementations (Mint and Metaphor that are built on object oriented languages (Java and C# respectively. The results concerning the application of this benchmark on these languages are presented and analysed. The measurement technique used in benchmarking leads to the development of a language independent performance enhancement framework that allows the programmer to select which code segments need staging. The framework also enables the programmer to verify the effectiveness of staging on the application performance. The framework is applied to a real case study. The case study results showed the effectiveness of the framework to achieve significant performance enhancement.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

Directory of Open Access Journals (Sweden)

Shaojian Qu

Full Text Available In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC. For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

Interactive Fuzzy Goal Programming approach in multi-response stratified sample surveys

Directory of Open Access Journals (Sweden)

Gupta Neha

2016-01-01

Full Text Available In this paper, we applied an Interactive Fuzzy Goal Programming (IFGP approach with linear, exponential and hyperbolic membership functions, which focuses on maximizing the minimum membership values to determine the preferred compromise solution for the multi-response stratified surveys problem, formulated as a Multi- Objective Non Linear Programming Problem (MONLPP, and by linearizing the nonlinear objective functions at their individual optimum solution, the problem is approximated to an Integer Linear Programming Problem (ILPP. A numerical example based on real data is given, and comparison with some existing allocations viz. Cochran’s compromise allocation, Chatterjee’s compromise allocation and Khowaja’s compromise allocation is made to demonstrate the utility of the approach.

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.

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

Technology of solving multi-objective problems of control of systems with distributed parameters

Science.gov (United States)

Rapoport, E. Ya.; Pleshivtseva, Yu. E.

2017-07-01

A constructive technology of multi-objective optimization of control of distributed parameter plants is proposed. The technology is based on a single-criterion version in the form of the minimax convolution of normalized performance criteria. The approach under development is based on the transition to an equivalent form of the variational problem with constraints, with the problem solution being a priori Pareto-effective. Further procedures of preliminary parameterization of control actions and subsequent reduction to a special problem of semi-infinite programming make it possible to find the sought extremals with the use of their Chebyshev properties and fundamental laws of the subject domain. An example of multi-objective optimization of operation modes of an engineering thermophysics object is presented, which is of independent interest.

Hybrid Multi-Objective Optimization of Folsom Reservoir Operation to Maximize Storage in Whole Watershed

Science.gov (United States)

Goharian, E.; Gailey, R.; Maples, S.; Azizipour, M.; Sandoval Solis, S.; Fogg, G. E.

2017-12-01

The drought incidents and growing water scarcity in California have a profound effect on human, agricultural, and environmental water needs. California experienced multi-year droughts, which have caused groundwater overdraft and dropping groundwater levels, and dwindling of major reservoirs. These concerns call for a stringent evaluation of future water resources sustainability and security in the state. To answer to this call, Sustainable Groundwater Management Act (SGMA) was passed in 2014 to promise a sustainable groundwater management in California by 2042. SGMA refers to managed aquifer recharge (MAR) as a key management option, especially in areas with high variation in water availability intra- and inter-annually, to secure the refill of underground water storage and return of groundwater quality to a desirable condition. The hybrid optimization of an integrated water resources system provides an opportunity to adapt surface reservoir operations for enhancement in groundwater recharge. Here, to re-operate Folsom Reservoir, objectives are maximizing the storage in the whole American-Cosumnes watershed and maximizing hydropower generation from Folsom Reservoir. While a linear programing (LP) module tends to maximize the total groundwater recharge by distributing and spreading water over suitable lands in basin, a genetic based algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), layer above it controls releases from the reservoir to secure the hydropower generation, carry-over storage in reservoir, available water for replenishment, and downstream water requirements. The preliminary results show additional releases from the reservoir for groundwater recharge during high flow seasons. Moreover, tradeoffs between the objectives describe that new operation performs satisfactorily to increase the storage in the basin, with nonsignificant effects on other objectives.

Multi-objective Optimization of a Solar Humidification Dehumidification Desalination Unit

Science.gov (United States)

Rafigh, M.; Mirzaeian, M.; Najafi, B.; Rinaldi, F.; Marchesi, R.

2017-11-01

In the present paper, a humidification-dehumidification desalination unit integrated with solar system is considered. In the first step mathematical model of the whole plant is represented. Next, taking into account the logical constraints, the performance of the system is optimized. On one hand it is desired to have higher energetic efficiency, while on the other hand, higher efficiency results in an increment in the required area for each subsystem which consequently leads to an increase in the total cost of the plant. In the present work, the optimum solution is achieved when the specific energy of the solar heater and also the areas of humidifier and dehumidifier are minimized. Due to the fact that considered objective functions are in conflict, conventional optimization methods are not applicable. Hence, multi objective optimization using genetic algorithm which is an efficient tool for dealing with problems with conflicting objectives has been utilized and a set of optimal solutions called Pareto front each of which is a tradeoff between the mentioned objectives is generated.

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...

Polar vessel hullform design based on the multi-objective optimization NSGA II

Directory of Open Access Journals (Sweden)

DUAN Fei

2017-12-01

Full Text Available [Objectives] With the increasing exploitation of the Arctic abundant oil and gas resources, a large number of ships which meet the polar navigational requirements are needed.[Methods] In this paper, the fast elitist Non-Dominated Sorting Genetic Algorithm (NSGA Ⅱ is applied to the hull optimization, and the multi-objective optimization method of polar vessel design is proposed. With the optimization goal of resistance and icebreaking resistance, filtering hull forms through the standard of polar vessel displacement and EEDI, fast ship hull optimization that satisfy the ice-ship dead weight and EEDI requirements has been achieved. Taking a 65 000 t shuttle tanker as an example, full parametric modeling method is adopted, the hull optimization of three different bow forms is conducted through the polar vessel multi-objective optimization method.[Results] The ship hull after optimization can satisfy the IA class navigation require, where the resistance in calm water decreases up to 12.94%, and the minimum propulsion power in ice field has a 27.36% reduction.[Conclusions] The feasibility and validity of the NSGA Ⅱ applying in polar vessel design is verified.

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.

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

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

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.

Multispecies genetic objectives in spatial conservation planning.

Science.gov (United States)

Nielsen, Erica S; Beger, Maria; Henriques, Romina; Selkoe, Kimberly A; von der Heyden, Sophie

2017-08-01

Growing threats to biodiversity and global alteration of habitats and species distributions make it increasingly necessary to consider evolutionary patterns in conservation decision making. Yet, there is no clear-cut guidance on how genetic features can be incorporated into conservation-planning processes, despite multiple molecular markers and several genetic metrics for each marker type to choose from. Genetic patterns differ between species, but the potential tradeoffs among genetic objectives for multiple species in conservation planning are currently understudied. We compared spatial conservation prioritizations derived from 2 metrics of genetic diversity (nucleotide and haplotype diversity) and 2 metrics of genetic isolation (private haplotypes and local genetic differentiation) in mitochondrial DNA of 5 marine species. We compared outcomes of conservation plans based only on habitat representation with plans based on genetic data and habitat representation. Fewer priority areas were selected for conservation plans based solely on habitat representation than on plans that included habitat and genetic data. All 4 genetic metrics selected approximately similar conservation-priority areas, which is likely a result of prioritizing genetic patterns across a genetically diverse array of species. Largely, our results suggest that multispecies genetic conservation objectives are vital to creating protected-area networks that appropriately preserve community-level evolutionary patterns. © 2016 Society for Conservation Biology.

Multi-dimensional optimization of a terawatt seeded tapered Free Electron Laser with a Multi-Objective Genetic Algorithm

Energy Technology Data Exchange (ETDEWEB)

Wu, Juhao, E-mail: jhwu@SLAC.Stanford.EDU [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Hu, Newman [Valley Christian High School, 100 Skyway Drive, San Jose, CA 95111 (United States); Setiawan, Hananiel [The Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI 48824 (United States); Huang, Xiaobiao; Raubenheimer, Tor O. [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Jiao, Yi [Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China); Yu, George [Columbia University, New York, NY 10027 (United States); Mandlekar, Ajay [California Institute of Technology, Pasadena, CA 91125 (United States); Spampinati, Simone [Sincrotrone Trieste S.C.p.A. di interesse nazionale, Strada Statale 14-km 163,5 in AREA Science Park, 34149 Basovizza, Trieste (Italy); Fang, Kun [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Chu, Chungming [The Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI 48824 (United States); Qiang, Ji [Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720 (United States)

2017-02-21

There is a great interest in generating high-power hard X-ray Free Electron Laser (FEL) in the terawatt (TW) level that can enable coherent diffraction imaging of complex molecules like proteins and probe fundamental high-field physics. A feasibility study of producing such X-ray pulses was carried out employing a configuration beginning with a Self-Amplified Spontaneous Emission FEL, followed by a “self-seeding” crystal monochromator generating a fully coherent seed, and finishing with a long tapered undulator where the coherent seed recombines with the electron bunch and is amplified to high power. The undulator tapering profile, the phase advance in the undulator break sections, the quadrupole focusing strength, etc. are parameters to be optimized. A Genetic Algorithm (GA) is adopted for this multi-dimensional optimization. Concrete examples are given for LINAC Coherent Light Source (LCLS) and LCLS-II-type systems. Analytical estimate is also developed to cross check the simulation and optimization results as a quick and complimentary tool.

Determination of radial profile of ICF hot spot's state by multi-objective parameters optimization

International Nuclear Information System (INIS)

Dong Jianjun; Deng Bo; Cao Zhurong; Ding Yongkun; Jiang Shaoen

2014-01-01

A method using multi-objective parameters optimization is presented to determine the radial profile of hot spot temperature and density. And a parameter space which contain five variables: the temperatures at center and the interface of fuel and remain ablator, the maximum model density of remain ablator, the mass ratio of remain ablator to initial ablator and the position of interface between fuel and the remain ablator, is used to described the hot spot radial temperature and density. Two objective functions are set as the variances of normalized intensity profile from experiment X-ray images and the theory calculation. Another objective function is set as the variance of experiment average temperature of hot spot and the average temperature calculated by theoretical model. The optimized parameters are obtained by multi-objective genetic algorithm searching for the five dimension parameter space, thereby the optimized radial temperature and density profiles can be determined. The radial temperature and density profiles of hot spot by experiment data measured by KB microscope cooperating with X-ray film are presented. It is observed that the temperature profile is strongly correlated to the objective functions. (authors)

Economic planning for electric energy systems: a multi objective linearized approach for solution

International Nuclear Information System (INIS)

Mata Medeiros Branco, T. da.

1986-01-01

The economic planning problem associated to the expansion and operation of electrical power systems is considered in this study, represented for a vectorial objective function in which the minimization of resources involved and maximization of attended demand constitute goals to be satisfied. Supposing all the variables involved with linear characteristic and considering the conflict existing among the objectives to be achieved, in order to find a solution, a multi objective linearized approach is proposed. This approximation utilizes the compromise programming technique and linear programming methods. Generation and transmission are simultaneously considered into the optimization process in which associated losses and the capacity of each line are included. Illustrated examples are also presented with results discussed. (author)

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.

Multi-objective congestion management by modified augmented ε-constraint method

International Nuclear Information System (INIS)

Esmaili, Masoud; Shayanfar, Heidar Ali; Amjady, Nima

2011-01-01

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented ε-constraint method, is based on the augmented ε-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multi-objective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method. (author)

Scientific discovery using genetic programming

DEFF Research Database (Denmark)

Keijzer, Maarten

2001-01-01

programming paradigm. The induction of mathematical expressions based on data is called symbolic regression. In this work, genetic programming is extended to not just fit the data i.e., get the numbers right, but also to get the dimensions right. For this units of measurement are used. The main contribution......Genetic Programming is capable of automatically inducing symbolic computer programs on the basis of a set of examples or their performance in a simulation. Mathematical expressions are a well-defined subset of symbolic computer programs and are also suitable for optimization using the genetic...... in this work can be summarized as: The symbolic expressions produced by genetic programming can be made suitable for analysis and interpretation by using units of measurements to guide or restrict the search. To achieve this, the following has been accomplished: A standard genetic programming system...

QoS Routing in Ad-Hoc Networks Using GA and Multi-Objective Optimization

Directory of Open Access Journals (Sweden)

Admir Barolli

2011-01-01

Full Text Available Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing.

Multi-objective compared to single-objective optimization with application to model validation and uncertainty quantification

Energy Technology Data Exchange (ETDEWEB)

Schulze-Riegert, R.; Krosche, M.; Stekolschikov, K. [Scandpower Petroleum Technology GmbH, Hamburg (Germany); Fahimuddin, A. [Technische Univ. Braunschweig (Germany)

2007-09-13

History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results are compared to a single-objective optimization method. (orig.)

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.

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.

Development of Curriculum Content for a Unique Career Ladder Multi-Entry/Multi-Exit Nursing Program. Final Report.

Science.gov (United States)

Rosbach, Ellen M.

A project was undertaken to develop the curriculum content for a unique career ladder multi-entry/multi-exit nursing program that would provide training for nurse aides, practical nurses, and registered nurses. The major objectives of the project were to conduct a review of the literature on curriculum materials presently in use, to develop 11…

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.

The Combined Multi-objective Optimization Design for a Light Guide Rod

International Nuclear Information System (INIS)

Yang, Yu-Sen; Fung, Rong-Fong; Shih, Chun-Yao; Chien, Hong-Yao

2013-01-01

The light guide rod (LGR) has been popularly used for the vehicles, and the automobile lamp industries need mass production to match this trend. This paper aims to develop a systemic way to find the best parameters' combination for the LGR, and the parameters are usually restricted to some levels and random values. In this paper, the LGR example with two optical performances of illuminance flux and uniformity is to be optimized by use of the real-coded genetic algorithm (RGA) and grey relational analysis (GRA). The illuminance flux and uniformity of the best parameters' combination are obtained and compared with the initial set. Comparisons with Taguchi-Grey can improve 5% of gain and comparisons with Pareto genetic algorithm (PaGA) can improve 1.7% of gain. The combined multi-objective optimization can saving 7% time and it is found that the new proposed method has positive gains in performances.

"Slit Mask Design for the Giant Magellan Telescope Multi-object Astronomical and Cosmological Spectrograph"

Science.gov (United States)

Williams, Darius; Marshall, Jennifer L.; Schmidt, Luke M.; Prochaska, Travis; DePoy, Darren L.

2018-01-01

The Giant Magellan Telescope Multi-object Astronomical and Cosmological Spectrograph (GMACS) is currently in development for the Giant Magellan Telescope (GMT). GMACS will employ slit masks with a usable diameter of approximately 0.450 m for the purpose of multi-slit spectroscopy. Of significant importance are the design constraints and parameters of the multi-object slit masks themselves as well as the means for mapping astronomical targets to physical mask locations. Analytical methods are utilized to quantify deformation effects on a potential slit mask due to thermal expansion and vignetting of target light cones. Finite element analysis (FEA) is utilized to simulate mask flexure in changing gravity vectors. The alpha version of the mask creation program for GMACS, GMACS Mask Simulator (GMS), a derivative of the OSMOS Mask Simulator (OMS), is introduced.

Multi-objective Calibration of DHSVM Based on Hydrologic Key Elements in Jinhua River Basin, East China

Science.gov (United States)

Pan, S.; Liu, L.; Xu, Y. P.

2017-12-01

Abstract: In physically based distributed hydrological model, large number of parameters, representing spatial heterogeneity of watershed and various processes in hydrologic cycle, are involved. For lack of calibration module in Distributed Hydrology Soil Vegetation Model, this study developed a multi-objective calibration module using Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) and based on parallel computing of Linux cluster for DHSVM (ɛP-DHSVM). In this study, two hydrologic key elements (i.e., runoff and evapotranspiration) are used as objectives in multi-objective calibration of model. MODIS evapotranspiration obtained by SEBAL is adopted to fill the gap of lack of observation for evapotranspiration. The results show that good performance of runoff simulation in single objective calibration cannot ensure good simulation performance of other hydrologic key elements. Self-developed ɛP-DHSVM model can make multi-objective calibration more efficiently and effectively. The running speed can be increased by more than 20-30 times via applying ɛP-DHSVM. In addition, runoff and evapotranspiration can be simulated very well simultaneously by ɛP-DHSVM, with superior values for two efficiency coefficients (0.74 for NS of runoff and 0.79 for NS of evapotranspiration, -10.5% and -8.6% for PBIAS of runoff and evapotranspiration respectively).

Multi objective optimization of line pack management of gas pipeline system

International Nuclear Information System (INIS)

Chebouba, A

2015-01-01

This paper addresses the Line Pack Management of the ''GZ1 Hassi R'mell-Arzew'' gas pipeline. For a gas pipeline system, the decision-making on the gas line pack management scenarios usually involves a delicate balance between minimization of the fuel consumption in the compression stations and maximizing gas line pack. In order to select an acceptable Line Pack Management of Gas Pipeline scenario from these two angles for ''GZ1 Hassi R'mell- Arzew'' gas pipeline, the idea of multi-objective decision-making has been introduced. The first step in developing this approach is the derivation of a numerical method to analyze the flow through the pipeline under transient isothermal conditions. In this paper, the solver NSGA-II of the modeFRONTIER, coupled with a matlab program was used for solving the multi-objective problem

Multi-objective parametric optimization of Inertance type pulse tube refrigerator using response surface methodology and non-dominated sorting genetic algorithm

Science.gov (United States)

Rout, Sachindra K.; Choudhury, Balaji K.; Sahoo, Ranjit K.; Sarangi, Sunil K.

2014-07-01

The modeling and optimization of a Pulse Tube Refrigerator is a complicated task, due to its complexity of geometry and nature. The aim of the present work is to optimize the dimensions of pulse tube and regenerator for an Inertance-Type Pulse Tube Refrigerator (ITPTR) by using Response Surface Methodology (RSM) and Non-Sorted Genetic Algorithm II (NSGA II). The Box-Behnken design of the response surface methodology is used in an experimental matrix, with four factors and two levels. The diameter and length of the pulse tube and regenerator are chosen as the design variables where the rest of the dimensions and operating conditions of the ITPTR are constant. The required output responses are the cold head temperature (Tcold) and compressor input power (Wcomp). Computational fluid dynamics (CFD) have been used to model and solve the ITPTR. The CFD results agreed well with those of the previously published paper. Also using the results from the 1-D simulation, RSM is conducted to analyse the effect of the independent variables on the responses. To check the accuracy of the model, the analysis of variance (ANOVA) method has been used. Based on the proposed mathematical RSM models a multi-objective optimization study, using the Non-sorted genetic algorithm II (NSGA-II) has been performed to optimize the responses.

Adaptive multi-objective Optimization scheme for cognitive radio resource management

KAUST Repository

Alqerm, Ismail

2014-12-01

Cognitive Radio is an intelligent Software Defined Radio that is capable to alter its transmission parameters according to predefined objectives and wireless environment conditions. Cognitive engine is the actuator that performs radio parameters configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance. The optimization relies on adapting radio transmission parameters to environment conditions using constrained optimization modeling called fitness functions in an iterative manner. These functions include minimizing power consumption, Bit Error Rate, delay and interference. On the other hand, maximizing throughput and spectral efficiency. Cross-layer optimization is exploited to access environmental parameters from all TCP/IP stack layers. AMOS uses adaptive Genetic Algorithm in terms of its parameters and objective weights as the vehicle of optimization. The proposed scheme has demonstrated quick response and efficiency in three different scenarios compared to other schemes. In addition, it shows its capability to optimize the performance of TCP/IP layers as whole not only the physical layer.

Multi-objective scheduling of electric vehicles in smart distribution system

International Nuclear Information System (INIS)

Zakariazadeh, Alireza; Jadid, Shahram; Siano, Pierluigi

2014-01-01

Highlights: • Environmental/economic operational scheduling of electric vehicles. • The Vehicle to Grid capability and the actual patterns of drivers are considered. • A novel conceptual model for an electric vehicle management system is proposed. - Abstract: When preparing for the widespread adoption of Electric Vehicles (EVs), an important issue is to use a proper EVs’ charging/discharging scheduling model that is able to simultaneously consider economic and environmental goals as well as technical constraints of distribution networks. This paper proposes a multi-objective operational scheduling method for charging/discharging of EVs in a smart distribution system. The proposed multi-objective framework, based on augmented ε-constraint method, aims at minimizing the total operational costs and emissions. The Vehicle to Grid (V2G) capability as well as the actual patterns of drivers are considered in order to generate the Pareto-optimal solutions. The Benders decomposition technique is used in order to solve the proposed optimization model and to convert the large scale mixed integer nonlinear problem into mixed-integer linear programming and nonlinear programming problems. The effectiveness of the proposed resources scheduling approach is tested on a 33-bus distribution test system over a 24-h period. The results show that the proposed EVs’ charging/discharging method can reduce both of operation cost and air pollutant emissions

Multi-criteria objective based climate change impact assessment for multi-purpose multi-reservoir systems

Science.gov (United States)

Müller, Ruben; Schütze, Niels

2014-05-01

Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A

Multi-objective optimization of the reactor coolant system

International Nuclear Information System (INIS)

Chen Lei; Yan Changqi; Wang Jianjun

2014-01-01

Background: Weight and size are important criteria in evaluating the performance of a nuclear power plant. It is of great theoretical value and engineering significance to reduce the weight and volume of the components for a nuclear power plant by the optimization methodology. Purpose: In order to provide a new method for the optimization of nuclear power plant multi-objective, the concept of the non-dominated solution was introduced. Methods: Based on the parameters of Qinshan I nuclear power plant, the mathematical models of the reactor core, the reactor vessel, the main pipe, the pressurizer and the steam generator were built and verified. The sensitivity analyses were carried out to study the influences of the design variables on the objectives. A modified non-dominated sorting genetic algorithm was proposed and employed to optimize the weight and the volume of the reactor coolant system. Results: The results show that the component mathematical models are reliable, the modified non-dominated sorting generic algorithm is effective, and the reactor inlet temperature is the most important variable which influences the distribution of the non-dominated solutions. Conclusion: The optimization results could provide a reference to the design of such reactor coolant system. (authors)

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.

Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

Directory of Open Access Journals (Sweden)

Jie Zhu

2017-01-01

Full Text Available A procedure based on MATLAB combined with ANSYS is presented and utilized for the multi-objective aerodynamic and structural optimization of horizontal-axis wind turbine (HAWT blades. In order to minimize the cost of energy (COE and improve the overall performance of the blades, materials of carbon fiber reinforced plastic (CFRP combined with glass fiber reinforced plastic (GFRP are applied. The maximum annual energy production (AEP, the minimum blade mass and the minimum blade cost are taken as three objectives. Main aerodynamic and structural characteristics of the blades are employed as design variables. Various design requirements including strain, deflection, vibration and buckling limits are taken into account as constraints. To evaluate the aerodynamic performances and the structural behaviors, the blade element momentum (BEM theory and the finite element method (FEM are applied in the procedure. Moreover, the non-dominated sorting genetic algorithm (NSGA II, which constitutes the core of the procedure, is adapted for the multi-objective optimization of the blades. To prove the efficiency and reliability of the procedure, a commercial 1.5 MW HAWT blade is used as a case study, and a set of trade-off solutions is obtained. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

Multi objective genetic algorithm to optimize the local heat treatment of a hardenable aluminum alloy

Science.gov (United States)

Piccininni, A.; Palumbo, G.; Franco, A. Lo; Sorgente, D.; Tricarico, L.; Russello, G.

2018-05-01

The continuous research for lightweight components for transport applications to reduce the harmful emissions drives the attention to the light alloys as in the case of Aluminium (Al) alloys, capable to combine low density with high values of the strength-to-weight ratio. Such advantages are partially counterbalanced by the poor formability at room temperature. A viable solution is to adopt a localized heat treatment by laser of the blank before the forming process to obtain a tailored distribution of material properties so that the blank can be formed at room temperature by means of conventional press machines. Such an approach has been extensively investigated for age hardenable alloys, but in the present work the attention is focused on the 5000 series; in particular, the optimization of the deep drawing process of the alloy AA5754 H32 is proposed through a numerical/experimental approach. A preliminary investigation was necessary to correctly tune the laser parameters (focus length, spot dimension) to effectively obtain the annealed state. Optimal process parameters were then obtained coupling a 2D FE model with an optimization platform managed by a multi-objective genetic algorithm. The optimal solution (i.e. able to maximize the LDR) in terms of blankholder force and extent of the annealed region was thus evaluated and validated through experimental trials. A good matching between experimental and numerical results was found. The optimal solution allowed to obtain an LDR of the locally heat treated blank larger than the one of the material either in the wrought condition (H32) either in the annealed condition (H111).

A Multi-Objective Demand Side Management Considering ENS Cost in Smart Grids

DEFF Research Database (Denmark)

Yousefi Khanghah, Babak; Ghassemzadeh, Saeid; Hosseini, Seyed Hossein

2017-01-01

In this paper a new method is presented to achieve economic exploitation and proper usage of network capacity by exerting controlling actions over flexible loads and energy storage (ES) equipment. Multi-objective planning for demand response programs (DRP) and battery management policies is carried...... out by considering energy not supplied (ENS). In order to achieve an optimal scheduling, charge/discharge control for batteries, demand response programs and dispatch of controllable distributed generations (DGs) are also considered. Then, the balanced cost and benefits of participants are evaluated...

Conserving energy in smallholder agriculture. A multi-objective programming case-study of northwest India

International Nuclear Information System (INIS)

Thankappan, Samarthia; Midmore, Peter; Jenkins, Tim

2006-01-01

In semi-arid conditions in Northwest India, smallholder agriculture has made increasing use of subsidised mechanisation and energy inputs to reduce short-term risks. However, detrimental environmental consequences have occurred, not least a rapidly falling water table, and energy-intensive production is threatened by the prospect of increasing scarcity and expense of energy supplies, especially as urban demands are forecast to grow rapidly. This paper describes the energy flows through four subsystems of smallholder agricultural villages: the crop system; non-crop land uses; livestock systems; and households. It employs a multi-objective programming model to demonstrate choices available for maximands either of net solar energy capture or financial surpluses. Applied to three villages selected to represent major settlement types in the Saurashtra region of Gujarat, the results demonstrate that both energy conservation and financial performance can be improved. Although these results need qualifying because of the reductionist, linear character of the model used, they do provide important insights into the cultural role of mechanisation and the influence of traditional agricultural practices. They also underline the need for local energy conservation strategies as part of an overall approach to improved self-determination in progress towards rural sustainability. (author)

Multi-objective optimization of organic Rankine cycles for waste heat recovery: Application in an offshore platform

DEFF Research Database (Denmark)

Pierobon, Leonardo; Nguyen, Tuong-Van; Larsen, Ulrik

2013-01-01

This paper aims at finding the optimal design of MW-size organic Rankine cycles by employing the multi-objective optimization with the genetic algorithm as the optimizer. We consider three objective functions: thermal efficiency, total volume of the system and net present value. The optimization...... for acetone. Other promising working fluids are cyclohexane, hexane and isohexane. The present methodology can be utilized in waste heat recovery applications where a compromise between performance, compactness and economic revenue is required. © 2013 Elsevier Ltd. All rights reserved....

StrateGene: object-oriented programming in molecular biology.

Science.gov (United States)

Carhart, R E; Cash, H D; Moore, J F

1988-03-01

This paper describes some of the ways that object-oriented programming methodologies have been used to represent and manipulate biological information in a working application. When running on a Xerox 1100 series computer, StrateGene functions as a genetic engineering workstation for the management of information about cloning experiments. It represents biological molecules, enzymes, fragments, and methods as classes, subclasses, and members in a hierarchy of objects. These objects may have various attributes, which themselves can be defined and classified. The attributes and their values can be passed from the classes of objects down to the subclasses and members. The user can modify the objects and their attributes while using them. New knowledge and changes to the system can be incorporated relatively easily. The operations on the biological objects are associated with the objects themselves. This makes it easier to invoke them correctly and allows generic operations to be customized for the particular object.

An Integrated Method for Interval Multi-Objective Planning of a Water Resource System in the Eastern Part of Handan

Directory of Open Access Journals (Sweden)

Meiqin Suo

2017-07-01

Full Text Available In this study, an integrated solving method is proposed for interval multi-objective planning. The proposed method is based on fuzzy linear programming and an interactive two-step method. It cannot only provide objectively optimal values for multiple objectives at the same time, but also effectively offer a globally optimal interval solution. Meanwhile, the degree of satisfaction related to different objective functions would be obtained. Then, the integrated solving method for interval multi-objective planning is applied to a case study of planning multi-water resources joint scheduling under uncertainty in the eastern part of Handan, China. The solutions obtained are useful for decision makers in easing the contradiction between supply of multi-water resources and demand from different water users. Moreover, it can provide the optimal comprehensive benefits of economy, society, and the environment.

Multi-objective calibration of a reservoir water quality model in aggregation and non-dominated sorting approaches

Science.gov (United States)

Huang, Yongtai

2014-03-01

Numerical water quality models are developed to predict contaminant fate and transport in receiving waters such as reservoirs and lakes. They can be helpful tools for water resource management. The objective of this study is to calibrate a water quality model which was set up to simulate the water quality conditions of Pepacton Reservoir, Downsville, New York, USA, using an aggregation hybrid genetic algorithm (AHGA) and a non-dominated sorting hybrid genetic algorithm (NSHGA). Both AHGA and NSHGA use a hybrid genetic algorithm (HGA) as optimization engines but are different in fitness assignment. In the AHGA, a weighted sum of scaled simulation errors is designed as an overall objective function to measure the fitness of solutions (i.e., parameter values). In the NSHGA, a method based on non-dominated sorting and Euclidean distances is proposed to calculate the dummy fitness of solutions. In addition, this study also compares the AHGA and the NSHGA. The purpose of this comparison is to determine whether the objective function values (i.e., simulation errors) and simulated results obtained by the AHGA and the NSHGA are significantly different from each other. The results show that the objective function values from the two HGAs are good compromises between all objective functions, and the calibrated model results match the observed data reasonably well and are comparable to other studies, supporting and justifying the use of multi-objective calibration.

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

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...

Multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using Genetic Algorithm

Science.gov (United States)

Kitagawa, Yosuke; Kitagawa, Koki; Nakamiya, Masaki; Kanazaki, Masahiro; Shimada, Toru

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

A procedure for multi-objective optimization of tire design parameters

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Nikola Korunović

2015-04-01

Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

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.

A Stochastic Multi-Objective Chance-Constrained Programming Model for Water Supply Management in Xiaoqing River Watershed

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Ye Xu

2017-05-01

Full Text Available In this paper, a stochastic multi-objective chance-constrained programming model (SMOCCP was developed for tackling the water supply management problem. Two objectives were included in this model, which are the minimization of leakage loss amounts and total system cost, respectively. The traditional SCCP model required the random variables to be expressed in the normal distributions, although their statistical characteristics were suitably reflected by other forms. The SMOCCP model allows the random variables to be expressed in log-normal distributions, rather than general normal form. Possible solution deviation caused by irrational parameter assumption was avoided and the feasibility and accuracy of generated solutions were ensured. The water supply system in the Xiaoqing River watershed was used as a study case for demonstration. Under the context of various weight combinations and probabilistic levels, many types of solutions are obtained, which are expressed as a series of transferred amounts from water sources to treated plants, from treated plants to reservoirs, as well as from reservoirs to tributaries. It is concluded that the SMOCCP model could reflect the sketch of the studied region and generate desired water supply schemes under complex uncertainties. The successful application of the proposed model is expected to be a good example for water resource management in other watersheds.

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.

COSMOS: Carnegie Observatories System for MultiObject Spectroscopy

Science.gov (United States)

Oemler, A.; Clardy, K.; Kelson, D.; Walth, G.; Villanueva, E.

2017-05-01

COSMOS (Carnegie Observatories System for MultiObject Spectroscopy) reduces multislit spectra obtained with the IMACS and LDSS3 spectrographs on the Magellan Telescopes. It can be used for the quick-look analysis of data at the telescope as well as for pipeline reduction of large data sets. COSMOS is based on a precise optical model of the spectrographs, which allows (after alignment and calibration) an accurate prediction of the location of spectra features. This eliminates the line search procedure which is fundamental to many spectral reduction programs, and allows a robust data pipeline to be run in an almost fully automatic mode, allowing large amounts of data to be reduced with minimal intervention.

Solving multi-objective facility location problem using the fuzzy analytical hierarchy process and goal programming: a case study on infectious waste disposal centers

Directory of Open Access Journals (Sweden)

Narong Wichapa

Full Text Available The selection of a suitable location for infectious waste disposal is one of the major problems in waste management. Determining the location of infectious waste disposal centers is a difficult and complex process because it requires combining social and environmental factors that are hard to interpret, and cost factors that require the allocation of resources. Additionally, it depends on several regulations. Based on the actual conditions of a case study, forty hospitals and three candidate municipalities in the sub-Northeast region of Thailand, we considered multiple factors such as infrastructure, geological and social & environmental factors, calculating global priority weights using the fuzzy analytical hierarchy process (FAHP. After that, a new multi-objective facility location problem model which combines FAHP and goal programming (GP, namely the FAHP-GP model, was tested. The proposed model can lead to selecting new suitable locations for infectious waste disposal by considering both total cost and final priority weight objectives. The novelty of the proposed model is the simultaneous combination of relevant factors that are difficult to interpret and cost factors, which require the allocation of resources. Keywords: Multi-objective facility location problem, Fuzzy analytic hierarchy process, Infectious waste disposal centers

Multi-period multi-objective electricity generation expansion planning problem with Monte-Carlo simulation

Energy Technology Data Exchange (ETDEWEB)

Tekiner, Hatice [Industrial Engineering, College of Engineering and Natural Sciences, Istanbul Sehir University, 2 Ahmet Bayman Rd, Istanbul (Turkey); Coit, David W. [Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Rd., Piscataway, NJ (United States); Felder, Frank A. [Edward J. Bloustein School of Planning and Public Policy, Rutgers University, Piscataway, NJ (United States)

2010-12-15

A new approach to the electricity generation expansion problem is proposed to minimize simultaneously multiple objectives, such as cost and air emissions, including CO{sub 2} and NO{sub x}, over a long term planning horizon. In this problem, system expansion decisions are made to select the type of power generation, such as coal, nuclear, wind, etc., where the new generation asset should be located, and at which time period expansion should take place. We are able to find a Pareto front for the multi-objective generation expansion planning problem that explicitly considers availability of the system components over the planning horizon and operational dispatching decisions. Monte-Carlo simulation is used to generate numerous scenarios based on the component availabilities and anticipated demand for energy. The problem is then formulated as a mixed integer linear program, and optimal solutions are found based on the simulated scenarios with a combined objective function considering the multiple problem objectives. The different objectives are combined using dimensionless weights and a Pareto front can be determined by varying these weights. The mathematical model is demonstrated on an example problem with interesting results indicating how expansion decisions vary depending on whether minimizing cost or minimizing greenhouse gas emissions or pollutants is given higher priority. (author)

Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller

Directory of Open Access Journals (Sweden)

Xiaozhang Qu

2016-07-01

Full Text Available A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction，the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system.

Object oriented programming

International Nuclear Information System (INIS)

Kunz, P.F.

1990-01-01

This paper is an introduction to object oriented programming techniques. It tries to explain the concepts by using analogies with traditional programming. The object oriented approach not inherently difficult, but most programmers find a relatively high threshold in learning it. Thus, this paper will attempt to convey the concepts with examples rather than explain the formal theory

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

Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

Science.gov (United States)

Muduli, Pradyut; Das, Sarat

2014-06-01

This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

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.

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.

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.

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.

Genetic programming in microorganisms

Energy Technology Data Exchange (ETDEWEB)

Hopwood, D A

1981-11-01

Formerly, when microbiologists had only existing organisms at their disposal whose characteristics could only be changed randomly by genetic experiments, they used to dream of programmed genetic changes. This dream has come true with modern genetic engineering.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

Science.gov (United States)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Programs as Data Objects

DEFF Research Database (Denmark)

This book constitutes the refereed proceedings of the Second Symposium on Programs as Data Objects, PADO 2001, held in Aarhus, Denmark, in May 2001. The 14 revised full papers presented were carefully reviewed and selected from 30 submissions. Various aspects of looking at programs as data objects...... are covered from the point of view of program analysis, program transformation, computational complexity, etc....

Multi-objective optimization of two alkali catalyzed processes for biodiesel from waste cooking oil

International Nuclear Information System (INIS)

Patle, Dipesh S.; Sharma, Shivom; Ahmad, Z.; Rangaiah, G.P.

2014-01-01

Highlights: • Biodiesel processes use waste cooking oil and are close to industrial practice. • Detailed constituents of waste cooking oil and detailed kinetics are used. • Two complete processes are optimized for economic and environmental objectives. • Obtained trade-offs provide deeper understanding and alternative optimal solutions. - Abstract: In view of the finite availability and environmental concerns of fossil fuels, biodiesel is one of the promising fuel alternatives. This study considers waste cooking palm oil with 6% free fatty acids (FFA) as feed-stock, which facilitates its better utilization and promotes sustainability. Two biodiesel production processes (both involving esterification catalyzed by sulfuric acid and trans-esterification catalyzed by sodium hydroxide) are compared for economic and environmental objectives. Firstly, these processes are simulated, considering detailed constituents of palm oil and also detailed kinetics for both esterification and trans-esterification, in Aspen Plus simulator. Subsequently, both the processes are optimized considering profit, heat duty and organic waste as objectives, and using an Excel-based multi-objective optimization (EMOO) program for the elitist non-dominated sorting genetic algorithm-II (NSGA-II). The results show that the profit improves with the increase in heat duty, and that the profit increase is accompanied by larger amount of organic waste. Process 1 having three trans-esterification reactors produces significantly lower organic waste (by 32%), requires lower heat duty (by 39%) and slightly more profitable (by 1.6%) compared to Process 2 having a single trans-esterification reactor and also a different separation sequence. Overall, the obtained quantitative trade-offs between objectives enable better decision making about the process design for biodiesel production from waste cooking oil

Multi-Label Object Categorization Using Histograms of Global Relations

DEFF Research Database (Denmark)

Mustafa, Wail; Xiong, Hanchen; Kraft, Dirk

2015-01-01

In this paper, we present an object categorization system capable of assigning multiple and related categories for novel objects using multi-label learning. In this system, objects are described using global geometric relations of 3D features. We propose using the Joint SVM method for learning......). The experiments are carried out on a dataset of 100 objects belonging to 13 visual and action-related categories. The results indicate that multi-label methods are able to identify the relation between the dependent categories and hence perform categorization accordingly. It is also found that extracting...

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...

Multi-objective generation scheduling with hybrid energy resources

Science.gov (United States)

Trivedi, Manas

emission targets. Since minimizing the emissions and fuel cost are conflicting objectives, a practical approach based on multi-objective optimization is applied to obtain compromised solutions in a single simulation run using genetic algorithm. These solutions are known as non-inferior or Pareto-optimal solutions, graphically illustrated by the trade-off curves between criterions fuel cost and pollutant emission. The efficacy of the proposed approach is illustrated with the help of different sample test cases. This research would be useful for society, electric utilities, consultants, regulatory bodies, policy makers and planners.

Multi-species genetic connectivity in a terrestrial habitat network.

Science.gov (United States)

Marrotte, Robby R; Bowman, Jeff; Brown, Michael G C; Cordes, Chad; Morris, Kimberley Y; Prentice, Melanie B; Wilson, Paul J

2017-01-01

Habitat fragmentation reduces genetic connectivity for multiple species, yet conservation efforts tend to rely heavily on single-species connectivity estimates to inform land-use planning. Such conservation activities may benefit from multi-species connectivity estimates, which provide a simple and practical means to mitigate the effects of habitat fragmentation for a larger number of species. To test the validity of a multi-species connectivity model, we used neutral microsatellite genetic datasets of Canada lynx ( Lynx canadensis ), American marten ( Martes americana ), fisher ( Pekania pennanti ), and southern flying squirrel ( Glaucomys volans ) to evaluate multi-species genetic connectivity across Ontario, Canada. We used linear models to compare node-based estimates of genetic connectivity for each species to point-based estimates of landscape connectivity (current density) derived from circuit theory. To our knowledge, we are the first to evaluate current density as a measure of genetic connectivity. Our results depended on landscape context: habitat amount was more important than current density in explaining multi-species genetic connectivity in the northern part of our study area, where habitat was abundant and fragmentation was low. In the south however, where fragmentation was prevalent, genetic connectivity was correlated with current density. Contrary to our expectations however, locations with a high probability of movement as reflected by high current density were negatively associated with gene flow. Subsequent analyses of circuit theory outputs showed that high current density was also associated with high effective resistance, underscoring that the presence of pinch points is not necessarily indicative of gene flow. Overall, our study appears to provide support for the hypothesis that landscape pattern is important when habitat amount is low. We also conclude that while current density is proportional to the probability of movement per unit area

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.

工业PTA氧化过程的多目标优化%Multi-objective Optimization of Industrial Purified Terephthalic Acid Oxidation Process

Institute of Scientific and Technical Information of China (English)

牟盛静; 苏宏业; 古勇; 褚健

2003-01-01

Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process model that has been proved to describe industrial process quite well. The model is a semiempirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithm applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.

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.

Searching for the Pareto frontier in multi-objective protein design.

Science.gov (United States)

Nanda, Vikas; Belure, Sandeep V; Shir, Ofer M

2017-08-01

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

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...

New multi-objective decision support methodology to solve problems of reconfiguration in the electric distribution systems

NARCIS (Netherlands)

Santos, S.F.; Paterakis, N.G.; Catalao, J.P.S.; Camarinha-Matos, L.M.; Baldissera, T.A.; Di Orio, G.; Marques, F.

2015-01-01

The distribution systems (DS) reconfiguration problem is formulated in this paper as a multi-objective mixed-integer linear programming (MILP) multiperiod problem, enforcing that the obtained topology is radial in order to exploit several advantages those configurations offer. The effects of

Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and price-quantity discounts in a multiple sourcing environment

Science.gov (United States)

Chang, Ching-Ter; Chen, Huang-Mu; Zhuang, Zheng-Yun

2014-05-01

Supplier selection (SS) is a multi-criteria and multi-objective problem, in which multi-segment (e.g. imperfect-quality discount (IQD) and price-quantity discount (PQD)) and multi-aspiration level problems may be significantly important; however, little attention had been given to dealing with both of them simultaneously in the past. This study proposes a model for integrating multi-choice goal programming and multi-segment goal programming to solve the above-mentioned problems by providing the following main contributions: (1) it allows decision-makers to set multiple aspiration levels on the right-hand side of each goal to suit real-world situations, (2) the PQD and IQD conditions are considered in the proposed model simultaneously and (3) the proposed model can solve a SS problem with n suppliers where each supplier offers m IQD with r PQD intervals, where only ? extra binary variables are required. The usefulness of the proposed model is explained using a real case. The results indicate that the proposed model not only can deal with a SS problem with multi-segment and multi-aspiration levels, but also can help the decision-maker to find the appropriate order quantities for each supplier by considering cost, quality and delivery.

Energy, exergy, economic (3E) analyses and multi-objective optimization of vapor absorption heat transformer using NSGA-II technique

International Nuclear Information System (INIS)

Jain, Vaibhav; Sachdeva, Gulshan

2017-01-01

Highlights: • Study includes energy, exergy and economic analyses of absorption heat transformer. • It addresses multi-objective optimization study using NSGA-II technique. • Total annual cost and total exergy destruction are simultaneously optimized. • Results with multi-objective optimized design are more acceptable than other. - Abstract: Present paper addresses the energy, exergy and economic (3E) analyses of absorption heat transformer (AHT) working with LiBr-H 2 O fluid pair. The heat exchangers namely absorber, condenser, evaporator, generator and solution heat exchanger are designed for the size and cost estimation of AHT. Later, the effect of operating variables is examined on the system performance, size and cost. Simulation studies showed a conflict between thermodynamic and economic performance of the system. The heat exchangers with lower investment cost showed high irreversible losses and vice versa. Thus, the operating variables of systems are determined economically as well as thermodynamically by implementing non-dominated sort genetic algorithm-II (NSGA-II) technique of multi-objective optimization. In present work, if the cost based optimized design is chosen, total exergy destruction is 2.4% higher than its minimum possible value; whereas, if total exergy based optimized design is chosen, total annual cost is 6.1% higher than its minimum possible value. On the other hands, total annual cost and total exergy destruction are only 1.0% and 0.8%, respectively more from their minimum possible values with multi-objective optimized design. Thus, the multi-objective optimized design of the AHT is best outcome than any other single-objective optimized designs.

A multi-objective framework for dynamic transmission expansion planning in competitive electricity market

International Nuclear Information System (INIS)

Foroud, Asghar Akbari; Abdoos, Ali Akbar; Keypour, Reza; Amirahmadi, Meisam

2010-01-01

Restructuring of power system has changed the traditional planning objectives and introduced challenges in the field of Transmission Expansion Planning (TEP). Due to these changes, new approaches and criteria are needed for transmission planning in deregulated environment. Therefore, in this paper, a dynamic expansion methodology is presented using a multi-objective optimization framework. Investment cost, congestion cost and reliability are considered in the optimization as three objectives. To overcome the difficulties in solving the non-convex and mixed integer nature of the optimization problems, a Non-Dominated Sorting Genetic Algorithm (NSGA II) approach is used followed by a fuzzy decision making analysis to obtain the final optimal solution. The planning methodology has been demonstrated on the IEEE 24-bus test system and north-east of Iran national 400 kV transmission grid to show the feasibility and capabilities of the proposed algorithm in electricity market environment. (author)

Landlord Program multi-year program plan fiscal year 1995 WBS 7.5

International Nuclear Information System (INIS)

Young, C.L.

1994-01-01

The Landlord Program mission is to maintain, preserve, or upgrade the strategic assets of the Hanford Site to meet the overall cleanup mission. This encompasses innovative, appropriate, and cost effective general purpose infrastructure support, services, and long range strategic site planning that is the foundation for seven major Hanford programs. These programs are (1) Environmental Restoration, (2) Tank Waste Remediation System, (3) Solid/Liquid Waste Decontamination, (4) Facility Transition, (5) Spent Fuel, (6) Technology Development, and (7) the Multi-Program Laboratory. General infrastructure support consists of facilities, systems, and equipment that by design or use are not essentially dedicated to a single program mission. Facilities include laboratories, shops, warehouses, and general work space. Systems include electrical, process sewers, rail, roads, telecommunications, water, fire and emergency response, and steam supply and distribution. Funding also supports capital equipment critical to maintaining, upgrading, or operating the general infrastructure. Paramount to these objectives is compliance with all applicable laws, orders, agreements, codes, standards, best management and safety practices. The objectives for general infrastructure support are reflected in five programmatic functions, (1) Program Integration, (2) Capital Equipment, (3) Expense Funded Projects, (4) General Plant Projects, and (5) Line Items

Exergoeconomic analysis and multi-objective optimization of an ejector refrigeration cycle powered by an internal combustion (HCCI) engine

International Nuclear Information System (INIS)

Sadeghi, Mohsen; Mahmoudi, S.M.S.; Khoshbakhti Saray, R.

2015-01-01

Highlights: • Ejector refrigeration systems powered by HCCI engine is proposed. • A new two-dimensional model is developed for the ejector. • Multi-objective optimization is performed for the proposed system. • Pareto frontier is plotted for multi-objective optimization. - Abstract: Ejector refrigeration systems powered by low-grade heat sources have been an attractive research subject for a lot of researchers. In the present work the waste heat from exhaust gases of a HCCI (homogeneous charge compression ignition) engine is utilized to drive the ejector refrigeration system. Considering the frictional effects on the ejector wall, a new two-dimensional model is developed for the ejector. Energy, exergy and exergoeconomic analysis performed for the proposed system using the MATLAB software. In addition, considering the exergy efficiency and the product unit cost of the system as objective functions, a multi-objective optimization is performed for the system to find the optimum design variables including the generator, condenser and evaporator temperatures. The product unit cost is minimized while the exergy efficiency is maximized using the genetic algorithm. The optimization results are obtained as a set of optimal points and the Pareto frontier is plotted for multi-objective optimization. The results of the optimization show that ejector refrigeration cycle is operating at optimum state based on exergy efficiency and product unit cost when generator, condenser and evaporator work at 94.54 °C, 33.44 °C and 0.03 °C, respectively

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

Science.gov (United States)

Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-Objective Optimization for Solid Amine CO2 Removal Assembly in Manned Spacecraft

Directory of Open Access Journals (Sweden)

Rong A

2017-07-01

Full Text Available Carbon Dioxide Removal Assembly (CDRA is one of the most important systems in the Environmental Control and Life Support System (ECLSS for a manned spacecraft. With the development of adsorbent and CDRA technology, solid amine is increasingly paid attention due to its obvious advantages. However, a manned spacecraft is launched far from the Earth, and its resources and energy are restricted seriously. These limitations increase the design difficulty of solid amine CDRA. The purpose of this paper is to seek optimal design parameters for the solid amine CDRA. Based on a preliminary structure of solid amine CDRA, its heat and mass transfer models are built to reflect some features of the special solid amine adsorbent, Polyethylenepolyamine adsorbent. A multi-objective optimization for the design of solid amine CDRA is discussed further in this paper. In this study, the cabin CO2 concentration, system power consumption and entropy production are chosen as the optimization objectives. The optimization variables consist of adsorption cycle time, solid amine loading mass, adsorption bed length, power consumption and system entropy production. The Improved Non-dominated Sorting Genetic Algorithm (NSGA-II is used to solve this multi-objective optimization and to obtain optimal solution set. A design example of solid amine CDRA in a manned space station is used to show the optimal procedure. The optimal combinations of design parameters can be located on the Pareto Optimal Front (POF. Finally, Design 971 is selected as the best combination of design parameters. The optimal results indicate that the multi-objective optimization plays a significant role in the design of solid amine CDRA. The final optimal design parameters for the solid amine CDRA can guarantee the cabin CO2 concentration within the specified range, and also satisfy the requirements of lightweight and minimum energy consumption.

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.

Objective Evaluation in an Online Geographic Information System Certificate Program

Directory of Open Access Journals (Sweden)

Scott L. WALKER

2005-01-01

Full Text Available Objective Evaluation in an Online Geographic Information System Certificate Program Asst. Professor. Dr. Scott L. WALKER Texas State University-San Marcos San Marcos, Texas, USA ABSTRACT Departmental decisions regarding distance education programs can be subject to subjective decision-making processes influenced by external factors such as strong faculty opinions or pressure to increase student enrolment. This paper outlines an evaluation of a departmental distance-education program. The evaluation utilized several methods that strived to inject objectivity in evaluation and subsequent decision-making. A rapid multi-modal approach included evaluation methods of (1 considering the online psychosocial learning environment, (2 content analyses comparing the online version of classes to face-to-face versions, (3 cost comparisons in online vs. face-to-face classes, (4 student outcomes, (5 student retention, and (6 benchmarking. These approaches offer opportunities for departmental administrators and decision-making committees to make judgments informed by facts rather than being influenced by the emotions, beliefs, or opinions of organizational dynamics.

Hybrid of Genetic Programming with PBIL

International Nuclear Information System (INIS)

Caldas, Gustavo Henrique Flores; Schirru, Roberto

2005-01-01

Genetic programming and PBIL (Population-Based Incremental Learning) are evolutionary algorithms that have found applications in several fields of application. The Genetic Programming searches a solution allowing that the individuals of a population modify, mainly, its structures. The PBIL, on the other hand, works with individuals of fixed structure and is particularly successful in finding numerical solutions. There are problems where the simultaneous adjustment of the structure and numerical constants in a solution is essential. The Symbolic Regression is an example where both the form and the constants of a mathematical expression must be found. Although the traditional Genetic Programming is capable to solve this problem by itself, it is interesting to explore a cooperation with the PBIL, allowing each algorithm to do only that they do best: the Genetic Programming tries to find a structure while the PBIL adjust the constants that will be enclosed in the structure. In this work, the benchmark 'the sextic polynomial regression problem' is used to compare some traditional techniques of Genetic Programming with the proposed Hybrid of Genetic Programming with PBIL. The results are presented and discussed. (author)

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

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.

Applications of Genetic Programming

DEFF Research Database (Denmark)

Gaunholt, Hans; Toma, Laura

1996-01-01

In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc.......In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc....

When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming

International Nuclear Information System (INIS)

Pan, Indranil; Das, Saptarshi

2015-01-01

Highlights: •New 3D continuous time chaotic systems with analytical expressions are obtained. •The multi-gene genetic programming (MGGP) paradigm is employed to achieve this. •Extends earlier works for evolving generalised family of Lorenz attractors. •Over one hundred of new chaotic attractors along with their parameters are reported. •The MGGP method have the potential for finding other similar chaotic attractors. -- Abstract: In this paper, we propose a novel methodology for automatically finding new chaotic attractors through a computational intelligence technique known as multi-gene genetic programming (MGGP). We apply this technique to the case of the Lorenz attractor and evolve several new chaotic attractors based on the basic Lorenz template. The MGGP algorithm automatically finds new nonlinear expressions for the different state variables starting from the original Lorenz system. The Lyapunov exponents of each of the attractors are calculated numerically based on the time series of the state variables using time delay embedding techniques. The MGGP algorithm tries to search the functional space of the attractors by aiming to maximise the largest Lyapunov exponent (LLE) of the evolved attractors. To demonstrate the potential of the proposed methodology, we report over one hundred new chaotic attractor structures along with their parameters, which are evolved from just the Lorenz system alone

Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

OpenAIRE

Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel

2016-01-01

Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than minimizing a single expected loss objective, and it may be more realistically discussed as a multi-objective optimization problem. We propose a novel generative model for scalar-valued utility functions to capture human preferences in a multi-objective optimization setting. We also outline an interactive active learn...

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

Multi-Year Program Plan - Building Regulatory Programs

Energy Technology Data Exchange (ETDEWEB)

none,

2010-10-01

This document presents DOE’s multi-year plan for the three components of the Buildings Regulatory Program: Appliance and Equipment Efficiency Standards, ENERGY STAR, and the Building Energy Codes Program. This document summarizes the history of these programs, the mission and goals of the programs, pertinent statutory requirements, and DOE’s 5-year plan for moving forward.

A study of the LCA based biofuel supply chain multi-objective optimization model with multi-conversion paths in China

International Nuclear Information System (INIS)

Liu, Zhexuan; Qiu, Tong; Chen, Bingzhen

2014-01-01

Highlights: • A LCA based biofuel supply chain model considering 3E criteria was proposed. • The model was used to design a supply chain considering three conversion pathways. • An experimental biofuel supply chain for China was designed. • A Pareto-optimal solution surface of this multi-objective problem was obtained. • The designed supply chain was rather robust to price variation. - Abstract: In this paper we present a life cycle assessment (LCA) based biofuel supply chain model with multi-conversion pathways. This model was formulated as a mixed integer linear programming (MILP) problem which took economic, energy, and environmental criteria (3E) into consideration. The economic objective was measured by the total annual profit. The energy objective was measured by using the average fossil energy input per megajoule (MJ) of biofuel. The environmental objective was measured by greenhouse gas (GHG) emissions per MJ of biofuel. After carefully consideration of the current situation in China, we chose to examine three conversion pathways: bio-ethanol (BE), bio-methanol (BM) and bio-diesel (BD). LCA was integrated to a multi-objective supply chain model by dividing each pathway into several individual parts and analyzing each part. The multi-objective MILP problem was solved using a ε-constraint method by defining the total annual profit as the optimization objective and assigning the average fossil energy input per MJ biofuel and GHG emissions per MJ biofuel as constraints. This model was then used to design an experimental biofuel supply chain for China. A surface of the Pareto optimal solutions was obtained by linear interpolation of the non-inferior solutions. The optimal results included the choice of optimal conversion pathway, biomass type, biomass locations, facility locations, and network topology structure in the biofuel supply chain. Distributed and centralized systems were also factored into our experimental system design. In addition, the

Multi-population Genomic Relationships for Estimating Current Genetic Variances Within and Genetic Correlations Between Populations.

Science.gov (United States)

Wientjes, Yvonne C J; Bijma, Piter; Vandenplas, Jérémie; Calus, Mario P L

2017-10-01

Different methods are available to calculate multi-population genomic relationship matrices. Since those matrices differ in base population, it is anticipated that the method used to calculate genomic relationships affects the estimate of genetic variances, covariances, and correlations. The aim of this article is to define the multi-population genomic relationship matrix to estimate current genetic variances within and genetic correlations between populations. The genomic relationship matrix containing two populations consists of four blocks, one block for population 1, one block for population 2, and two blocks for relationships between the populations. It is known, based on literature, that by using current allele frequencies to calculate genomic relationships within a population, current genetic variances are estimated. In this article, we theoretically derived the properties of the genomic relationship matrix to estimate genetic correlations between populations and validated it using simulations. When the scaling factor of across-population genomic relationships is equal to the product of the square roots of the scaling factors for within-population genomic relationships, the genetic correlation is estimated unbiasedly even though estimated genetic variances do not necessarily refer to the current population. When this property is not met, the correlation based on estimated variances should be multiplied by a correction factor based on the scaling factors. In this study, we present a genomic relationship matrix which directly estimates current genetic variances as well as genetic correlations between populations. Copyright © 2017 by the Genetics Society of America.

A Bayesian alternative for multi-objective ecohydrological model specification

Science.gov (United States)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

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.

Scalable and practical multi-objective distribution network expansion planning

NARCIS (Netherlands)

Luong, N.H.; Grond, M.O.W.; Poutré, La J.A.; Bosman, P.A.N.

2015-01-01

We formulate the distribution network expansion planning (DNEP) problem as a multi-objective optimization (MOO) problem with different objectives that distribution network operators (DNOs) would typically like to consider during decision making processes for expanding their networks. Objectives are

OBJECT-SPACE MULTI-IMAGE MATCHING OF MOBILE-MAPPING-SYSTEM IMAGE SEQUENCES

Directory of Open Access Journals (Sweden)

Y. C. Chen

2012-07-01

Full Text Available This paper proposes an object-space multi-image matching procedure of terrestrial MMS (Mobile Mapping System image sequences to determine the coordinates of an object point automatically and reliably. This image matching procedure can be applied to find conjugate points of MMS image sequences efficiently. Conventional area-based image matching methods are not reliable to deliver accurate matching results for this application due to image scale variations, viewing angle variations, and object occlusions. In order to deal with these three matching problems, an object space multi-image matching is proposed. A modified NCC (Normalized Cross Correlation coefficient is proposed to measure the similarity of image patches. A modified multi-window matching procedure will also be introduced to solve the problem of object occlusion. A coarse-to-fine procedure with a combination of object-space multi-image matching and multi-window matching is adopted. The proposed procedure has been implemented for the purpose of matching terrestrial MMS image sequences. The ratio of correct matches of this experiment was about 80 %. By providing an approximate conjugate point in an overlapping image manually, most of the incorrect matches could be fixed properly and the ratio of correct matches was improved up to 98 %.

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.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System

KAUST Repository

Charara, Ali; Ltaief, Hatem; Gratadour, Damien; Keyes, David E.; Sevin, Arnaud; Abdelfattah, Ahmad; Gendron, Eric; Morel, Carine; Vidal, Fabrice

2014-01-01

called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used

A multi-objective decision framework for lifecycle investment

NARCIS (Netherlands)

Timmermans, S.H.J.T.; Schumacher, J.M.; Ponds, E.H.M.

2017-01-01

In this paper we propose a multi-objective decision framework for lifecycle investment choice. Instead of optimizing individual strategies with respect to a single-valued objective, we suggest evaluation of classes of strategies in terms of the quality of the tradeoffs that they provide. The

Object-Oriented Programming in the Beta Programming Language

DEFF Research Database (Denmark)

Madsen, Ole Lehrmann; Møller-Pedersen, Birger; Nygaard, Kristen

This is a book on object-oriented programming and the BETA programming language. Object-oriented programming originated with the Simula languages developed at the Norwegian Computing Center, Oslo, in the 1960s. The first Simula language, Simula I, was intended for writing simulation programs....... Simula I was later used as a basis for defining a general purpose programming language, Simula 67. In addition to being a programming language, Simula1 was also designed as a language for describing and communicating about systems in general. Simula has been used by a relatively small community for many...... years, although it has had a major impact on research in computer science. The real breakthrough for object-oriented programming came with the development of Smalltalk. Since then, a large number of programming languages based on Simula concepts have appeared. C++ is the language that has had...

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.

Linear genetic programming

CERN Document Server

Brameier, Markus

2007-01-01

Presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. This book serves as a reference for researchers, but also contains sufficient introduction for students and those who are new to the field

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

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

An interval-parameter mixed integer multi-objective programming for environment-oriented evacuation management

Science.gov (United States)

Wu, C. Z.; Huang, G. H.; Yan, X. P.; Cai, Y. P.; Li, Y. P.

2010-05-01

Large crowds are increasingly common at political, social, economic, cultural and sports events in urban areas. This has led to attention on the management of evacuations under such situations. In this study, we optimise an approximation method for vehicle allocation and route planning in case of an evacuation. This method, based on an interval-parameter multi-objective optimisation model, has potential for use in a flexible decision support system for evacuation management. The modeling solutions are obtained by sequentially solving two sub-models corresponding to lower- and upper-bounds for the desired objective function value. The interval solutions are feasible and stable in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving decision makers' estimates under different conditions. The resulting model can be used for a systematic analysis of the complex relationships among evacuation time, cost and environmental considerations. The results of a case study used to validate the proposed model show that the model does generate useful solutions for planning evacuation management and practices. Furthermore, these results are useful for evacuation planners, not only in making vehicle allocation decisions but also for providing insight into the tradeoffs among evacuation time, environmental considerations and economic objectives.

Genetic variability of broodstocks of restocking programs in Brazil

Directory of Open Access Journals (Sweden)

Nelson Lopera-Barrero

2015-09-01

Full Text Available Objective. The aim of this study was evaluate the genetic diversity of the following broodstocks: piapara (Leporinus elongatus, dourado (Salminus brasiliensis, jundiá (Rhamdia quelen and cachara (Pseudoplatystoma fasciatum already useful for restocking programs in the Paranapanema, Iguaçu and Paraná Brazilian Rivers. Materials and methods. Samples from the caudal fin of 122 fish were analyzed. DNA was extracted by NaCl protocol. PCR products were separated by a horizontal agarose gel electrophoresis. The fragments were visualized by staining with ethidium bromide. Results. The amplification of 25 primers generated different fragments in studied species that allowed characterizing 440 fragments of 100-2900 bp. High percentage of polymorphic fragments (66.67 to 86.29, Shannon index (0.365 to 0.486 and genetic diversity of Nei (0.248 to 0.331 were detected. Conclusions. The level of genetic variability in the broodstocks was adequate for allowing their use in restocking programs in the studied Rivers. However, periodical monitoring studies of genetic variability in these stocks, the mating system, reproductive system and general management must be made to guarantee the preservation of wild populations.

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

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.

Beginning C# Object-Oriented Programming

CERN Document Server

Clark, Dan

2011-01-01

Beginning C# Object-Oriented Programming brings you into the modern world of development as you master the fundamentals of programming with C# and learn to develop efficient, reusable, elegant code through the object-oriented programming (OOP) methodology. Take your skills out of the 20th century and into this one with Dan Clark's accessible, quick-paced guide to C# and object-oriented programming, completely updated for .NET 4.0 and C# 4.0. As you develop techniques and best practices for coding in C#, one of the world's most popular contemporary languages, you'll experience modeling a "real

Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

Directory of Open Access Journals (Sweden)

M. Amiri

2012-10-01

Full Text Available Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on Nasdaq from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods.

Programming in an object-oriented environment

CERN Document Server

Ege, Raimund K

1992-01-01

Programming in an Object-Oriented Environment provides an in-depth look at the concepts behind the technology of object-oriented programming.This book explains why object-oriented programming has the potential to vastly improve the productivity of programmers and how to apply this technology in a practical environment. Many programming examples are included, focusing on how different programming languages support the core of object-oriented concepts. C++ is used as the main sample language throughout this text.This monograph consists of two major parts. Part I provides an introduction to objec

Parametric analysis of energy quality management for district in China using multi-objective optimization approach

International Nuclear Information System (INIS)

Lu, Hai; Yu, Zitao; Alanne, Kari; Xu, Xu; Fan, Liwu; Yu, Han; Zhang, Liang; Martinac, Ivo

2014-01-01

Highlights: • A time-effective multi-objective design optimization scheme is proposed. • The scheme aims at exploring suitable 3E energy system for the specific case. • A realistic case located in China is used for the analysis. • Parametric study is investigated to test the effects of different parameters. - Abstract: Due to the increasing energy demands and global warming, energy quality management (EQM) for districts has been getting importance over the last few decades. The evaluation of the optimum energy systems for specific districts is an essential part of EQM. This paper presents a deep analysis of the optimum energy systems for a district sited in China. A multi-objective optimization approach based on Genetic Algorithm (GA) is proposed for the analysis. The optimization process aims to search for the suitable 3E (minimum economic cost and environmental burden as well as maximum efficiency) energy systems. Here, life cycle CO 2 equivalent (LCCO 2 ), life cycle cost (LCC) and exergy efficiency (EE) are set as optimization objectives. Then, the optimum energy systems for the Chinese case are presented. The final work is to investigate the effects of different energy parameters. The results show the optimum energy systems might vary significantly depending on some parameters

Theoretical and algorithmic advances in multi-parametric programming and control

KAUST Repository

Pistikopoulos, Efstratios N.; Dominguez, Luis; Panos, Christos; Kouramas, Konstantinos; Chinchuluun, Altannar

2012-01-01

This paper presents an overview of recent theoretical and algorithmic advances, and applications in the areas of multi-parametric programming and explicit/multi-parametric model predictive control (mp-MPC). In multi-parametric programming, advances include areas such as nonlinear multi-parametric programming (mp-NLP), bi-level programming, dynamic programming and global optimization for multi-parametric mixed-integer linear programming problems (mp-MILPs). In multi-parametric/explicit MPC (mp-MPC), advances include areas such as robust multi-parametric control, multi-parametric nonlinear MPC (mp-NMPC) and model reduction in mp-MPC. A comprehensive framework for multi-parametric programming and control is also presented. Recent applications include a hydrogen storage device, a fuel cell power generation system, an unmanned autonomous vehicle (UAV) and a hybrid pressure swing adsorption (PSA) system. © 2012 Springer-Verlag.

Theoretical and algorithmic advances in multi-parametric programming and control

KAUST Repository

Pistikopoulos, Efstratios N.

2012-04-21

This paper presents an overview of recent theoretical and algorithmic advances, and applications in the areas of multi-parametric programming and explicit/multi-parametric model predictive control (mp-MPC). In multi-parametric programming, advances include areas such as nonlinear multi-parametric programming (mp-NLP), bi-level programming, dynamic programming and global optimization for multi-parametric mixed-integer linear programming problems (mp-MILPs). In multi-parametric/explicit MPC (mp-MPC), advances include areas such as robust multi-parametric control, multi-parametric nonlinear MPC (mp-NMPC) and model reduction in mp-MPC. A comprehensive framework for multi-parametric programming and control is also presented. Recent applications include a hydrogen storage device, a fuel cell power generation system, an unmanned autonomous vehicle (UAV) and a hybrid pressure swing adsorption (PSA) system. © 2012 Springer-Verlag.

Comparative study on credibility measures of type-2 and type-1 fuzzy variables and their application to a multi-objective profit transportation problem via goal programming

Directory of Open Access Journals (Sweden)

Dipak Kumar Jana

2017-06-01

Full Text Available In real world applications supply, demand and transportation costs per unit of the quantities in multi-objective transportation problems may be hardly specified accurately because of the changing economic and environmental conditions. It is also significant that the time required for transportation should be minimized. In this paper, we have presented three reduction methods for a type-2 triangular fuzzy variable (T2TrFV by adopting the critical value (CV. Three generalized expected values (optimistic, CV and pessimistic are derived for T2TrFVs with some special cases. Then a multi-objective profit transportation problem (MOPTP with fixed charge (FC cost has been formulated and solved in type-2 fuzzy environment. Unit transportation costs, FC, selling prices, unit transport times, loading and unloading times, total supply capacities and demands are all considered as triangular Type-2 fuzzy numbers. The MOPTP has been converted into a single objective by using the goal programming technique and the weighted sum method. The deterministic model is then solved using the Generalized Reduced Gradient method Lingo 14.0. Numerical experiments with some sensitivity analysis are illustrated the application and effectiveness of the proposed approaches.

Joint Conditional Random Field Filter for Multi-Object Tracking

Directory of Open Access Journals (Sweden)

Luo Ronghua

2011-03-01

Full Text Available Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF.

A procedure for multi-objective optimization of tire design parameters

OpenAIRE

Nikola Korunović; Miloš Madić; Miroslav Trajanović; Miroslav Radovanović

2015-01-01

The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zo...

Near-Earth object hazardous impact: A Multi-Criteria Decision Making approach.

Science.gov (United States)

Sánchez-Lozano, J M; Fernández-Martínez, M

2016-11-16

The impact of a near-Earth object (NEO) may release large amounts of energy and cause serious damage. Several NEO hazard studies conducted over the past few years provide forecasts, impact probabilities and assessment ratings, such as the Torino and Palermo scales. These high-risk NEO assessments involve several criteria, including impact energy, mass, and absolute magnitude. The main objective of this paper is to provide the first Multi-Criteria Decision Making (MCDM) approach to classify hazardous NEOs. Our approach applies a combination of two methods from a widely utilized decision making theory. Specifically, the Analytic Hierarchy Process (AHP) methodology is employed to determine the criteria weights, which influence the decision making, and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain a ranking of alternatives (potentially hazardous NEOs). In addition, NEO datasets provided by the NASA Near-Earth Object Program are utilized. This approach allows the classification of NEOs by descending order of their TOPSIS ratio, a single quantity that contains all of the relevant information for each object.

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...

An intuitionistic fuzzy multi-objective non-linear programming model for sustainable irrigation water allocation under the combination of dry and wet conditions

Science.gov (United States)

Li, Mo; Fu, Qiang; Singh, Vijay P.; Ma, Mingwei; Liu, Xiao

2017-12-01

Water scarcity causes conflicts among natural resources, society and economy and reinforces the need for optimal allocation of irrigation water resources in a sustainable way. Uncertainties caused by natural conditions and human activities make optimal allocation more complex. An intuitionistic fuzzy multi-objective non-linear programming (IFMONLP) model for irrigation water allocation under the combination of dry and wet conditions is developed to help decision makers mitigate water scarcity. The model is capable of quantitatively solving multiple problems including crop yield increase, blue water saving, and water supply cost reduction to obtain a balanced water allocation scheme using a multi-objective non-linear programming technique. Moreover, it can deal with uncertainty as well as hesitation based on the introduction of intuitionistic fuzzy numbers. Consideration of the combination of dry and wet conditions for water availability and precipitation makes it possible to gain insights into the various irrigation water allocations, and joint probabilities based on copula functions provide decision makers an average standard for irrigation. A case study on optimally allocating both surface water and groundwater to different growth periods of rice in different subareas in Heping irrigation area, Qing'an County, northeast China shows the potential and applicability of the developed model. Results show that the crop yield increase target especially in tillering and elongation stages is a prevailing concern when more water is available, and trading schemes can mitigate water supply cost and save water with an increased grain output. Results also reveal that the water allocation schemes are sensitive to the variation of water availability and precipitation with uncertain characteristics. The IFMONLP model is applicable for most irrigation areas with limited water supplies to determine irrigation water strategies under a fuzzy environment.

Advanced Object-Oriented Programming in R

DEFF Research Database (Denmark)

Mailund, Thomas

2017-01-01

Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object......-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software. After reading Advanced Object-Oriented Programming in R, you'll come away with a practical project that you can reuse in your own analytics coding...... of data being manipulated. You will: Define and use classes and generic functions using R Work with the R class hierarchies Benefit from implementation reuse Handle operator overloading Apply the S4 and R6 classes...

Advanced Object-Oriented Programming in R

DEFF Research Database (Denmark)

Mailund, Thomas

Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object......-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software. After reading Advanced Object-Oriented Programming in R, you'll come away with a practical project that you can reuse in your own analytics coding...... of data being manipulated. You will: Define and use classes and generic functions using R Work with the R class hierarchies Benefit from implementation reuse Handle operator overloading Apply the S4 and R6 classes...

Multi-Objective Weather Routing of Sailing Vessels

Directory of Open Access Journals (Sweden)

Życzkowski Marcin

2017-12-01

Full Text Available The paper presents a multi-objective deterministic method of weather routing for sailing vessels. Depending on a particular purpose of sailboat weather routing, the presented method makes it possible to customize the criteria and constraints so as to fit a particular user’s needs. Apart from a typical shortest time criterion, safety and comfort can also be taken into account. Additionally, the method supports dynamic weather data: in its present version short-term, mid-term and long-term term weather forecasts are used during optimization process. In the paper the multi-objective optimization problem is first defined and analysed. Following this, the proposed method solving this problem is described in detail. The method has been implemented as an online SailAssistance application. Some representative examples solutions are presented, emphasizing the effects of applying different criteria or different values of customized parameters.

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.

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.

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

A multi-objective approach for developing national energy efficiency plans

International Nuclear Information System (INIS)

Haydt, Gustavo; Leal, Vítor; Dias, Luís

2014-01-01

This paper proposes a new approach to deal with the problem of building national energy efficiency (EE) plans, considering multiple objectives instead of only energy savings. The objectives considered are minimizing the influence of energy use on climate change, minimizing the financial risk from the investment, maximizing the security of energy supply, minimizing investment costs, minimizing the impacts of building new power plants and transmission infrastructures, and maximizing the local air quality. These were identified through literature review and interaction with real decision makers. A database of measures is established, from which millions of potential EE plans can be built by combining measures and their respective degree of implementation. Finally, a hybrid multi-objective and multi-criteria decision analysis (MCDA) model is proposed to search and select the EE plans that best match the decision makers’ preferences. An illustration of the working mode and the type of results obtained from this novel hybrid model is provided through an application to Portugal. For each of five decision perspectives a wide range of potential best plans were identified. These wide ranges show the relevance of introducing multi-objective analysis in a comprehensive search space as a tool to inform decisions about national EE plans. - Highlights: • A multiple objective approach to aid the choice of national energy efficiency plans. • A hybrid multi-objective MCDA model is proposed to search among the possible plans. • The model identified relevant plans according to five different idealized DMs. • The approach is tested with Portugal

Object-oriented programming with gradual abstraction

DEFF Research Database (Denmark)

Nørmark, Kurt; Thomsen, Lone Leth; Thomsen, Bent

2013-01-01

We describe an experimental object-oriented programming language, ASL2, that supports program development by means of a series of abstraction steps. The language allows immediate object construction, and it is possible to use the constructed objects for concrete problem solving tasks. Classes...... restrictive. As a central mechanism, weakly classified objects are allowed to borrow methods from each other. ASL2 supports class generalization, as a counterpart to class specialization and inheritance in mainstream object-oriented programming languages. The final abstraction step discussed in this paper...

OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

Directory of Open Access Journals (Sweden)

Y. Jia

2016-06-01

Full Text Available The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

Multi-objective energy management optimization and parameter sizing for proton exchange membrane hybrid fuel cell vehicles

International Nuclear Information System (INIS)

Hu, Zunyan; Li, Jianqiu; Xu, Liangfei; Song, Ziyou; Fang, Chuan; Ouyang, Minggao; Dou, Guowei; Kou, Gaihong

2016-01-01

Highlights: • Fuel economy, lithium battery size and powertrain system durability are incorporated in optimization. • A multi-objective power allocation strategy by taking battery size into consideration is proposed. • Influences of battery capacity and auxiliary power on strategy design are explored. • Battery capacity and fuel cell service life for the system life cycle cost are optimized. - Abstract: The powertrain system of a typical proton electrolyte membrane hybrid fuel cell vehicle contains a lithium battery package and a fuel cell stack. A multi-objective optimization for this powertrain system of a passenger car, taking account of fuel economy and system durability, is discussed in this paper. Based on an analysis of the optimum results obtained by dynamic programming, a soft-run strategy was proposed for real-time and multi-objective control algorithm design. The soft-run strategy was optimized by taking lithium battery size into consideration, and implemented using two real-time algorithms. When compared with the optimized dynamic programming results, the power demand-based control method proved more suitable for powertrain systems equipped with larger capacity batteries, while the state of charge based control method proved superior in other cases. On this basis, the life cycle cost was optimized by considering both lithium battery size and equivalent hydrogen consumption. The battery capacity selection proved more flexible, when powertrain systems are equipped with larger capacity batteries. Finally, the algorithm has been validated in a fuel cell city bus. It gets a good balance of fuel economy and system durability in a three months demonstration operation.

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.

Dependent-Chance Programming Models for Capital Budgeting in Fuzzy Environments

Institute of Scientific and Technical Information of China (English)

LIANG Rui; GAO Jinwu

2008-01-01

Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting with fuzzy input data and multiple conflicting objectives. When the decision maker sets a prospec-tive profit level and wants to maximize the chances of the total profit achieving the prospective profit level, a fuzzy dependent-chance programming model, a fuzzy multi-objective dependent-chance programming model, and a fuzzy goal dependent-chance programming model are used to formulate the fuzzy capital budgeting problem. A fuzzy simulation based genetic algorithm is used to solve these models. Numerical examples are provided to illustrate the effectiveness of the simulation-based genetic algorithm and the po-tential applications of these models.

Multi-objective calibration of a reservoir model: aggregation and non-dominated sorting approaches

Science.gov (United States)

Huang, Y.

2012-12-01

Numerical reservoir models can be helpful tools for water resource management. These models are generally calibrated against historical measurement data made in reservoirs. In this study, two methods are proposed for the multi-objective calibration of such models: aggregation and non-dominated sorting methods. Both methods use a hybrid genetic algorithm as an optimization engine and are different in fitness assignment. In the aggregation method, a weighted sum of scaled simulation errors is designed as an overall objective function to measure the fitness of solutions (i.e. parameter values). The contribution of this study to the aggregation method is the correlation analysis and its implication to the choice of weight factors. In the non-dominated sorting method, a novel method based on non-dominated sorting and the method of minimal distance is used to calculate the dummy fitness of solutions. The proposed methods are illustrated using a water quality model that was set up to simulate the water quality of Pepacton Reservoir, which is located to the north of New York City and is used for water supply of city. The study also compares the aggregation and the non-dominated sorting methods. The purpose of this comparison is not to evaluate the pros and cons between the two methods but to determine whether the parameter values, objective function values (simulation errors) and simulated results obtained are significantly different with each other. The final results (objective function values) from the two methods are good compromise between all objective functions, and none of these results are the worst for any objective function. The calibrated model provides an overall good performance and the simulated results with the calibrated parameter values match the observed data better than the un-calibrated parameters, which supports and justifies the use of multi-objective calibration. The results achieved in this study can be very useful for the calibration of water

Open Issues in Object-Oriented Programming

DEFF Research Database (Denmark)

Madsen, Ole Lehrmann

1995-01-01

We discuss a number of open issues within object-oriented programming. The central mechanisms of object-oriented programming appeared with Simula, developed more than 30 years ago; these include class, subclass, virtual function, active object and the first application framework, Class Simulation....... The core parts of object-oriented programming should be well understood, but there are still a large number of issues where there is no consensus. The term object-orientation has been applied to many subjects, such as analysis, design implementation, data modeling in databases, and distribution...

Coastal aquifer management based on surrogate models and multi-objective optimization

Science.gov (United States)

Mantoglou, A.; Kourakos, G.

2011-12-01

The demand for fresh water in coastal areas and islands can be very high, especially in summer months, due to increased local needs and tourism. In order to satisfy demand, a combined management plan is proposed which involves: i) desalinization (if needed) of pumped water to a potable level using reverse osmosis and ii) injection of biologically treated waste water into the aquifer. The management plan is formulated into a multiobjective optimization framework, where simultaneous minimization of economic and environmental costs is desired; subject to a constraint to satisfy demand. The method requires modeling tools, which are able to predict the salinity levels of the aquifer in response to different alternative management scenarios. Variable density models can simulate the interaction between fresh and saltwater; however, they are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNN)]. The surrogate models are trained adaptively during optimization based on a Genetic Algorithm. In the crossover step of the genetic algorithm, each pair of parents generates a pool of offspring. All offspring are evaluated based on the fast surrogate model. Then only the most promising offspring are evaluated based on the exact numerical model. This eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. Three new criteria for selecting the most promising offspring were proposed, which improve the Pareto set and maintain the diversity of the optimum solutions. The method has important advancements compared to previous methods, e.g. alleviation of propagation of errors due to surrogate model approximations. The method is applied to a real coastal aquifer in the island of Santorini which is a very touristy island with high water demands. The results show that the algorithm

Research and application of multi-agent genetic algorithm in tower defense game

Science.gov (United States)

Jin, Shaohua

2018-04-01

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.

Evaluation of scalarization methods and NSGA-II/SPEA2 genetic algorithms for multi-objective optimization of green supply chain design

NARCIS (Netherlands)

C. van der Plas (Corne); T. Tervonen (Tommi); R. Dekker (Rommert)

2012-01-01

textabstractThis paper considers supply chain design in green logistics. We formulate the choice of an environmentally conscious chain design as a multi-objective optimization (MOO) problem and approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods as well

A fuzzy multi-objective linear programming approach for integrated sheep farming and wildlife in land management decisions: a case study in the Patagonian rangelands

Science.gov (United States)

Metternicht, Graciela; Blanco, Paula; del Valle, Hector; Laterra, Pedro; Hardtke, Leonardo; Bouza, Pablo

2015-04-01

Wildlife is part of the Patagonian rangelands sheep farming environment, with the potential of providing extra revenue to livestock owners. As sheep farming became less profitable, farmers and ranchers could focus on sustainable wildlife harvesting. It has been argued that sustainable wildlife harvesting is ecologically one of the most rational forms of land use because of its potential to provide multiple products of high value, while reducing pressure on ecosystems. The guanaco (Lama guanicoe) is the most conspicuous wild ungulate of Patagonia. Guanaco ?bre, meat, pelts and hides are economically valuable and have the potential to be used within the present Patagonian context of production systems. Guanaco populations in South America, including Patagonia, have experienced a sustained decline. Causes for this decline are related to habitat alteration, competition for forage with sheep, and lack of reasonable management plans to develop livelihoods for ranchers. In this study we propose an approach to explicitly determinate optimal stocking rates based on trade-offs between guanaco density and livestock grazing intensity on rangelands. The focus of our research is on finding optimal sheep stocking rates at paddock level, to ensure the highest production outputs while: a) meeting requirements of sustainable conservation of guanacos over their minimum viable population; b) maximizing soil carbon sequestration, and c) minimizing soil erosion. In this way, determination of optimal stocking rate in rangelands becomes a multi-objective optimization problem that can be addressed using a Fuzzy Multi-Objective Linear Programming (MOLP) approach. Basically, this approach converts multi-objective problems into single-objective optimizations, by introducing a set of objective weights. Objectives are represented using fuzzy set theory and fuzzy memberships, enabling each objective function to adopt a value between 0 and 1. Each objective function indicates the satisfaction of

Energy quality management for building clusters and districts (BCDs) through multi-objective optimization

International Nuclear Information System (INIS)

Lu, Hai; Alanne, Kari; Martinac, Ivo

2014-01-01

Highlights: • Energy quality management is applied from individual building to district. • A novel time-effective multi-objective design optimization scheme is proposed. • The scheme searches for exergy efficient and environmental solution for districts. • System reliability is considered and addressed in this paper. - Abstract: Renewable energy systems entail a significant potential to meet the energy requirements of building clusters and districts (BCDs) provided that local energy sources are exploited efficiently. Besides improving the energy efficiency by reducing energy consumption and improving the match between energy supply and demand, energy quality issues have become a key topic of interest. Energy quality management is a technique that aims at optimally utilizing the exergy content of various renewable energy sources. In addition to minimizing life-cycle CO 2 emissions related to exergy losses of an energy system, issues such as system reliability should be addressed. The present work contributes to the research by proposing a novel multi-objective design optimization scheme that minimizes the global warming potential during the life-cycle and maximizes the exergy performance, while the maximum allowable level of the loss of power supply probability (LPSP) is predefined by the user as a constraint. The optimization makes use of Genetic Algorithm (GA). Finally, a case study is presented, where the above methodology has been applied to an office BCD located in Norway. The proposed optimization scheme is proven to be efficient in finding the optimal design and can be easily enlarged to encompass more relevant objective functions

Genetic programming theory and practice XII

CERN Document Server

Riolo, Rick; Kotanchek, Mark

2015-01-01

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer funct

A hybrid input–output multi-objective model to assess economic–energy–environment trade-offs in Brazil

International Nuclear Information System (INIS)

Carvalho, Ariovaldo Lopes de; Antunes, Carlos Henggeler; Freire, Fausto; Henriques, Carla Oliveira

2015-01-01

A multi-objective linear programming (MOLP) model based on a hybrid Input–Output (IO) framework is presented. This model aims at assessing the trade-offs between economic, energy, environmental (E3) and social objectives in the Brazilian economic system. This combination of multi-objective models with Input–Output Analysis (IOA) plays a supplementary role in understanding the interactions between the economic and energy systems, and the corresponding impacts on the environment, offering a consistent framework for assessing the effects of distinct policies on these systems. Firstly, the System of National Accounts (SNA) is reorganized to include the National Energy Balance, creating a hybrid IO framework that is extended to assess Greenhouse Gas (GHG) emissions and the employment level. The objective functions considered are the maximization of GDP (gross domestic product) and employment levels, as well as the minimization of energy consumption and GHG emissions. An interactive method enabling a progressive and selective search of non-dominated solutions with distinct characteristics and underlying trade-offs is utilized. Illustrative results indicate that the maximization of GDP and the employment levels lead to an increase of both energy consumption and GHG emissions, while the minimization of either GHG emissions or energy consumption cause negative impacts on GDP and employment. - Highlights: • A hybrid Input–Output multi-objective model is applied to the Brazilian economy. • Objective functions are GDP, employment level, energy consumption and GHG emissions. • Interactive search process identifies trade-offs between the competing objectives. • Positive correlations between GDP growth and employment. • Positive correlations between energy consumption and GHG emissions

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.

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.

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

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

Object-oriented Programming Laws for Annotated Java Programs

Directory of Open Access Journals (Sweden)

Gabriel Falconieri Freitas

2010-03-01

Full Text Available Object-oriented programming laws have been proposed in the context of languages that are not combined with a behavioral interface specification language (BISL. The strong dependence between source-code and interface specifications may cause a number of difficulties when transforming programs. In this paper we introduce a set of programming laws for object-oriented languages like Java combined with the Java Modeling Language (JML. The set of laws deals with object-oriented features taking into account their specifications. Some laws deal only with features of the specification language. These laws constitute a set of small transformations for the development of more elaborate ones like refactorings.

Multi-objective optimization for integrated hydro–photovoltaic power system

International Nuclear Information System (INIS)

Li, Fang-Fang; Qiu, Jun

2016-01-01

Highlights: • A model optimizing both quality and quantity of hydro/PV power was proposed. • The dimension was reduced by decoupling hydropower and PV power in time scales. • Reservoir operations have been optimized for different typical hydrological years. • Hydropower was proved to be an ideal compensating resource for PV power in nature. - Abstract: The most striking feature of the solar energy is its intermittency and instability resulting from environmental influence. Hydropower can be an ideal choice to compensate photovoltaic (PV) power since it is easy to adjust and responds rapidly with low cost. This study proposed a long-term multi-objective optimization model for integrated hydro/PV power system considering the smoothness of power output process and the total amount of annual power generation of the system simultaneously. The PV power output is firstly calculated by hourly solar radiation and temperature data, which is then taken as the boundary condition for reservoir optimization. For hydropower, due to its great adjustable capability, a month is taken as the time step to balance the simulation cost. The problem dimension is thus reduced by decoupling hydropower and PV power in time scales. The modified version of Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to optimize the multi-objective problem. The proposed model was applied to the Longyangxia hydro/PV hybrid power system in Qinghai province of China, which is supposed to be the largest hydro/PV hydropower station in the world. The results verified that the hydropower is an ideal compensation resource for the PV power in nature, especially in wet years, when the solar radiation decreases due to rainfalls while the water resource is abundant to be allocated. The power generation potential is provided for different hydrologic years, which can be taken to evaluate the actual operations. The proposed methodology is general in that it can be used for other hydro/PV power systems

Feature-Oriented Programming with Object Algebras

NARCIS (Netherlands)

B.C.d.S. Oliveira (Bruno); T. van der Storm (Tijs); A. Loh; W.R. Cook

2013-01-01

htmlabstractObject algebras are a new programming technique that enables a simple solution to basic extensibility and modularity issues in programming languages. While object algebras excel at defining modular features, the composition mechanisms for object algebras (and features) are still

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

Multi-objective and multi-criteria optimization for power generation expansion planning with CO2 mitigation in Thailand

Directory of Open Access Journals (Sweden)

Kamphol Promjiraprawat

2013-06-01

Full Text Available In power generation expansion planning, electric utilities have encountered the major challenge of environmental awareness whilst being concerned with budgetary burdens. The approach for selecting generating technologies should depend on economic and environmental constraint as well as externalities. Thus, the multi-objective optimization becomes a more attractive approach. This paper presents a hybrid framework of multi-objective optimization and multi-criteria decision making to solve power generation expansion planning problems in Thailand. In this paper, CO2 emissions and external cost are modeled as a multi-objective optimization problem. Then the analytic hierarchy process is utilized to determine thecompromised solution. For carbon capture and storage technology, CO2 emissions can be mitigated by 74.7% from the least cost plan and leads to the reduction of the external cost of around 500 billion US dollars over the planning horizon. Results indicate that the proposed approach provides optimum cost-related CO2 mitigation plan as well as external cost.

Multi-objective Search-based Mobile Testing

OpenAIRE

Mao, K.

2017-01-01

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis...

Multi-objective decision-making model based on CBM for an aircraft fleet

Science.gov (United States)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

Integrated production planning and control: A multi-objective optimization model

Directory of Open Access Journals (Sweden)

Cheng Wang

2013-09-01

Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise

Exploring the trade-off between competing objectives for electricity energy retailers through a novel multi-objective framework

International Nuclear Information System (INIS)

Charwand, Mansour; Ahmadi, Abdollah; Siano, Pierluigi; Dargahi, Vahid; Sarno, Debora

2015-01-01

Highlights: • Proposing a new stochastic multi-objective framework for an electricity retailer. • Proposing a MIP model for an electricity retailer problem. • Employing ε-constraint method to generate Pareto solution. - Abstract: Energy retailer is the intermediary between Generation Companies and consumers. In the medium time horizon, in order to gain market share, he has to minimize his selling price while looking at the profit, which is dependent on the revenues from selling and the costs to buy energy from forward contracts and participation in the market pool. In this paper, the two competing objectives are engaged proposing a new multi-objective framework in which a ε-constraint mathematical technique is used to produce the Pareto front (set of optimal solutions). The stochasticity of energy prices in the market and customer load demand are coped with the Lattice Monte Carlo Simulation (LMCS) and the method of the roulette wheel, which allow the stochastic multi-objective problem to be turned into a set of deterministic equivalents. The method performance is tested into some case studies

Use of interactive data visualization in multi-objective forest planning.

Science.gov (United States)

Haara, Arto; Pykäläinen, Jouni; Tolvanen, Anne; Kurttila, Mikko

2018-03-15

Common to multi-objective forest planning situations is that they all require comparisons, searches and evaluation among decision alternatives. Through these actions, the decision maker can learn from the information presented and thus make well-justified decisions. Interactive data visualization is an evolving approach that supports learning and decision making in multidimensional decision problems and planning processes. Data visualization contributes the formation of mental image data and this process is further boosted by allowing interaction with the data. In this study, we introduce a multi-objective forest planning decision problem framework and the corresponding characteristics of data. We utilize the framework with example planning data to illustrate and evaluate the potential of 14 interactive data visualization techniques to support multi-objective forest planning decisions. Furthermore, broader utilization possibilities of these techniques to incorporate the provisioning of ecosystem services into forest management and planning are discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

Incorporating social benefits in multi-objective optimization of forest-based bioenergy and biofuel supply chains

International Nuclear Information System (INIS)

Cambero, Claudia; Sowlati, Taraneh

2016-01-01

Highlights: • Quantified social benefits of forest- based biomass supply chain. • Developed multi-objective optimization model. • Incorporated social benefits into multi-objective model. • Solved the model using the AUGMECON method. • Applied the model to a case study in Canada. - Abstract: Utilization of forest and wood residues to produce bioenergy and biofuels could generate additional revenue streams for forestry companies, reduce their environmental impacts and generate new development opportunities for forest-dependent communities. Further development of forest-based biorefineries entails addressing complexities and challenges related to biomass procurement, logistics, technologies, and sustainability. Numerous optimization models have been proposed for the economic and environmental design of biomass-to-bioenergy or biofuel supply chains. A few of them also maximized the job creation potential of the supply chain through the use of employment multipliers. The use of a total job creation indicator as the social optimization objective implies that all new jobs generate the same level of social benefit. In this paper, we quantify the potential social benefit of new forest-based biorefinery supply chains considering different impacts of new jobs based on their type and location. This social benefit is incorporated into a multi-objective mixed integer linear programming model that maximizes the social benefit, net present value and greenhouse gas emission saving potential of a forest-based biorefinery supply chain. The applicability of the model is illustrated through a case study in the interior region of British Columbia, Canada where different utilization paths for available forest and wood residues are investigated. The multi-objective optimization model is solved using a Pareto-generating method. The analysis of the generated set of Pareto-optimal solutions show a trade-off between the net present value of the supply chain and the other two

Multi-Criteria Layout Synthesis of MEMS Devices Using Memetic Computing

DEFF Research Database (Denmark)

Tutum, Cem Celal; Fan, Zhun

2011-01-01

This paper introduces a multi-objective optimization approach for layout synthesis of MEMS components. A case study of layout synthesis of a comb-driven micro-resonator shows that the approach proposed in this paper can lead to design results accommodating two design objectives, i.e. simultaneous...... sorting genetic algorithm (NSGA-II), has been applied to find multiple trade-off solutions followed by a gradient-based local search, i.e. sequential quadratic programming (SQP), to improve the convergence of the obtained Pareto-optimal front. In order to reduce the number of function evaluations...

Multi-objective optimization for generating a weighted multi-model ensemble

Science.gov (United States)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

Multi-element analysis of unidentified fallen objects from Tatale in ...

African Journals Online (AJOL)

A multi-element analysis has been carried out on two fallen objects, # 01 and # 02, using instrumental neutron activation analysis technique. A total of 17 elements were identified in object # 01 while 21 elements were found in object # 02. The two major elements in object # 01 were Fe and Mg, which together constitute ...

Large-scale hydropower system optimization using dynamic programming and object-oriented programming: the case of the Northeast China Power Grid.

Science.gov (United States)

Li, Ji-Qing; Zhang, Yu-Shan; Ji, Chang-Ming; Wang, Ai-Jing; Lund, Jay R

2013-01-01

This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results.

Femoral hip prosthesis design for Thais using multi-objective shape optimization

International Nuclear Information System (INIS)

Virulsri, Chanyaphan; Tangpornprasert, Pairat; Romtrairat, Parineak

2015-01-01

Highlights: • A multi-objective shape optimization was proposed to design hip prosthesis for Thais. • The prosthesis design was optimized in terms of safety of both cement and prosthesis. • The objective functions used the Soderberg fatigue strength formulations. • Safety factors of the cement and prosthesis are 1.200 and 1.109 respectively. • The newly designed prosthesis also fits well with chosen small-sized Thai femurs. - Abstract: The long-term success of Total Hip Arthroplasty (THA) depends largely on how well the prosthetic components fit the bones. The majority of cemented femoral hip prosthesis failures are due to aseptic loosening, which is possibly caused by cracking of the cement mantle. The strength of cement components is a function of cement mantles having adequate thickness. Since the size and shape of cemented femoral hip prostheses used in Thailand are based on designs for a Caucasian population, they do not properly conform to most Thai patients’ physical requirements. For these reasons, prostheses designed specifically for Thai patients must consider the longevity and functionality of both cement and prosthesis. The objective of this study was to discover a new design for femoral hip prostheses which is not only optimal and safe in terms of both cement and prosthesis, but also fits the selected Thai femur. This study used a small-sized Thai femoral model as a reference model for a new design. Biocompatible stainless steel 316L (SS316L) and polymethylmethacrylate (PMMA) were selected as raw materials for the prosthesis and bone cement respectively. A multi-objective shape optimization program, which is an interface between optimization C program named NSGA-II and a finite element program named ANSYS, was used to optimize longevity of femoral hip prostheses by varying shape parameters at assigned cross-sections of the selected geometry. Maximum walking loads of sixty-kilograms were applied to a finite element model for stress and

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

Directory of Open Access Journals (Sweden)

Vito Janko

2017-12-01

Full Text Available The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

Science.gov (United States)

Janko, Vito; Luštrek, Mitja

2017-12-29

The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

Optical cryptography with biometrics for multi-depth objects.

Science.gov (United States)

Yan, Aimin; Wei, Yang; Hu, Zhijuan; Zhang, Jingtao; Tsang, Peter Wai Ming; Poon, Ting-Chung

2017-10-11

We propose an optical cryptosystem for encrypting images of multi-depth objects based on the combination of optical heterodyne technique and fingerprint keys. Optical heterodyning requires two optical beams to be mixed. For encryption, each optical beam is modulated by an optical mask containing either the fingerprint of the person who is sending, or receiving the image. The pair of optical masks are taken as the encryption keys. Subsequently, the two beams are used to scan over a multi-depth 3-D object to obtain an encrypted hologram. During the decryption process, each sectional image of the 3-D object is recovered by convolving its encrypted hologram (through numerical computation) with the encrypted hologram of a pinhole image that is positioned at the same depth as the sectional image. Our proposed method has three major advantages. First, the lost-key situation can be avoided with the use of fingerprints as the encryption keys. Second, the method can be applied to encrypt 3-D images for subsequent decrypted sectional images. Third, since optical heterodyning scanning is employed to encrypt a 3-D object, the optical system is incoherent, resulting in negligible amount of speckle noise upon decryption. To the best of our knowledge, this is the first time optical cryptography of 3-D object images has been demonstrated in an incoherent optical system with biometric keys.

Introducing Object-Oriented Programming

DEFF Research Database (Denmark)

Caspersen, Michael Edelgaard

2008-01-01

The practice of teaching programming at universities, colleges and high schools went through a major change roughly in the mid 1990s: The teaching of objectorientation in introductory courses slowly became mainstream. Fairly soon, the Object First or Objects Early school of thought was formulated...

Behavioral program synthesis with genetic programming

CERN Document Server

Krawiec, Krzysztof

2016-01-01

Genetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities' and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck' leaves search algorithm underinformed about the actual and potential qualities of candidate programs. This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subs...

32 CFR 634.5 - Program objectives.

Science.gov (United States)

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Program objectives. 634.5 Section 634.5 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) LAW ENFORCEMENT AND CRIMINAL INVESTIGATIONS MOTOR VEHICLE TRAFFIC SUPERVISION Introduction § 634.5 Program objectives. (a) The objectives of motor vehicle traffic...

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

International Nuclear Information System (INIS)

Zu Yun-Xiao; Zhou Jie

2012-01-01

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. (geophysics, astronomy, and astrophysics)

Multi-Language Programs. Beginnings Workshop.

Science.gov (United States)

D'Onofrio-Papadaki, Evienia; Matsalia, Joan; Bowie, Paula; Wardle, Francis; Bruno, Holly Elissa

2003-01-01

Presents five articles on multi-language programs in early childhood education: "Bilingualism/Multilingualism and Language Acquisition Theories" (Evienia Papadaki-D'Onofrio); "Training and Supporting Caregivers Who Speak a Language Different from Those in Their Community" (Joan Matsalia and Paula Bowie); "Language Immersion Programs for Young…

Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

Directory of Open Access Journals (Sweden)

Yasuaki Miyazato

2016-12-01

Full Text Available A smart house generally has a Photovoltaic panel (PV, a Heat Pump (HP, a Solar Collector (SC and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions.

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.

Object recognition through a multi-mode fiber

Science.gov (United States)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach.

Science.gov (United States)

Zhang, Rui

2017-12-25

The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars.

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.

Genetic algorithms and their applications in accelerator physics

Energy Technology Data Exchange (ETDEWEB)

Hofler, Alicia S. [JLAB

2013-12-01

Multi-objective optimization techniques are widely used in an extremely broad range of fields. Genetic optimization for multi-objective optimization was introduced in the accelerator community in relatively recent times and quickly spread becoming a fundamental tool in multi-dimensional optimization problems. This discussion introduces the basics of the technique and reviews applications in accelerator problems.

Complex engineering objects construction using Multi-D innovative technology

International Nuclear Information System (INIS)

Agafonov, Alexey

2013-01-01

Multi-D technology is an integrated innovative project management system for a construction of complex engineering objects based on a construction process simulation using an intellectual 3D model. Multi-D technology includes: • The unified schedule of E+P+C; • The schedule of loading of human resources, machines & mechanisms; • The budget of expenses and the income integrated with the schedule; • 3D model; • Multi-D model; • Weekly-daily tasks (with 4th level schedules); • Control system of interaction of Customer-EPC(m) company - Contractors; • Change and configuration management system

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.

Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

Directory of Open Access Journals (Sweden)

Safiyullah Ferozkhan

2016-01-01

Full Text Available Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM. The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

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...

Multi-objective synthesis of work and heat exchange networks: Optimal balance between economic and environmental performance

International Nuclear Information System (INIS)

Onishi, Viviani C.; Ravagnani, Mauro A.S.S.; Jiménez, Laureano; Caballero, José A.

2017-01-01

Highlights: • New multi-objective optimization model for the simultaneous WHEN synthesis. • A multistage superstructure allows power and thermal integration of process streams. • Simultaneous minimization of environmental impacts and total annualized cost. • Alternative set of Pareto solutions is presented to support decision-makers. - Abstract: Sustainable and efficient energy use is crucial for lessening carbon dioxide emissions in industrial plants. This paper introduces a new multi-objective optimization model for the synthesis of work and heat exchange networks (WHENs), aiming to obtain the optimal balance between economic and environmental performance. The proposed multistage superstructure allows power and thermal integration of process gaseous streams, through the simultaneous minimization of total annualized cost (TAC) and environmental impacts (EI). The latter objective is determined by environmental indicators that follow the life cycle assessment (LCA) principles. The WHEN superstructure is optimized as a multi-objective mixed-integer nonlinear programming (moMINLP) model and solved with the GAMS software. Results show a decrease of ∼79% in the heat transfer area and ∼32% in the capital cost between the solutions found for single problem optimizations. These results represent a diminution of ∼23.5% in the TAC, while EI is increased in ∼99.2%. As these solutions can be impractical for economic or environmental reasons, we present a set of alternative Pareto-optimal solutions to support decision-makers towards the implementation of more environment-friendly and cost-effective WHENs.

Testing object Interactions

NARCIS (Netherlands)

Grüner, Andreas

2010-01-01

In this thesis we provide a unit testing approach for multi-purposes object-oriented programming languages in the style of Java and C#. Our approach includes the definition of a test specification language which results from extending the programming language with new designated specification

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.

Robust Multi-Objective PQ Scheduling for Electric Vehicles in Flexible Unbalanced Distribution Grids

DEFF Research Database (Denmark)

Knezovic, Katarina; Soroudi, Alireza; Marinelli, Mattia

2017-01-01

With increased penetration of distributed energy resources and electric vehicles (EVs), different EV management strategies can be used for mitigating adverse effects and supporting the distribution grid. This paper proposes a robust multi-objective methodology for determining the optimal day...... demand response programs. The method is tested on a real Danish unbalanced distribution grid with 35% EV penetration to demonstrate the effectiveness of the proposed approach. It is shown that the proposed formulation guarantees an optimal EV cost as long as the price uncertainties are lower than....... The robust formulation effectively considers the errors in the electricity price forecast and its influence on the EV schedule. Moreover, the impact of EV reactive power support on objective values and technical parameters is analysed both when EVs are the only flexible resources and when linked with other...

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.

Compressed multi-block local binary pattern for object tracking

Science.gov (United States)

Li, Tianwen; Gao, Yun; Zhao, Lei; Zhou, Hao

2018-04-01

Both robustness and real-time are very important for the application of object tracking under a real environment. The focused trackers based on deep learning are difficult to satisfy with the real-time of tracking. Compressive sensing provided a technical support for real-time tracking. In this paper, an object can be tracked via a multi-block local binary pattern feature. The feature vector was extracted based on the multi-block local binary pattern feature, which was compressed via a sparse random Gaussian matrix as the measurement matrix. The experiments showed that the proposed tracker ran in real-time and outperformed the existed compressive trackers based on Haar-like feature on many challenging video sequences in terms of accuracy and robustness.

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.

Ethics Programs and Ethical Cultures: A Next Step in Unraveling their Multi-Faceted Relationship

NARCIS (Netherlands)

S.P. Kaptein (Muel)

2008-01-01

textabstractThe objective of an ethics program is to improve the ethical culture of an organization. To date, empirical research treats at least one of these concepts as a one-dimensional construct. This paper demonstrates that by conceptualizing both constructs as multi-dimensional, a better

7 CFR 3560.52 - Program objectives.

Science.gov (United States)

2010-01-01

... objectives. The Agency uses appropriated funds to finance the construction, rehabilitation of program... 7 Agriculture 15 2010-01-01 2010-01-01 false Program objectives. 3560.52 Section 3560.52 Agriculture Regulations of the Department of Agriculture (Continued) RURAL HOUSING SERVICE, DEPARTMENT OF...

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...

Teaching Object-Oriented Programming is more than teaching Object-Oriented Programming Languages

DEFF Research Database (Denmark)

Knudsen, Jørgen Lindskov; Madsen, Ole Lehrmann

1988-01-01

the research area gives additional insight into the research area and its underlying theoretical foundation. In this paper we will report on our approach to teaching programming languages as a whole and especially teaching object-oriented programming. The prime message to be told is that working from...... a theoretical foundation pays off. Without a theoretical foundation, the discussions are often centered around features of different languages. With a foundation, discussions may be conducted on solid pound. Furthermore, the students have significantly fewer difficulties in grasping the concrete programming...

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

A scalable coevolutionary multi-objective particle swarm optimizer

Directory of Open Access Journals (Sweden)

Xiangwei Zheng

2010-11-01

Full Text Available Multi-Objective Particle Swarm Optimizers (MOPSOs are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and ?-dominance based MOPSO (CEPSO is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on ?-dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.

Multi-Objective Optimization of a Turbofan for an Advanced, Single-Aisle Transport

Science.gov (United States)

Berton, Jeffrey J.; Guynn, Mark D.

2012-01-01

Considerable interest surrounds the design of the next generation of single-aisle commercial transports in the Boeing 737 and Airbus A320 class. Aircraft designers will depend on advanced, next-generation turbofan engines to power these airplanes. The focus of this study is to apply single- and multi-objective optimization algorithms to the conceptual design of ultrahigh bypass turbofan engines for this class of aircraft, using NASA s Subsonic Fixed Wing Project metrics as multidisciplinary objectives for optimization. The independent design variables investigated include three continuous variables: sea level static thrust, wing reference area, and aerodynamic design point fan pressure ratio, and four discrete variables: overall pressure ratio, fan drive system architecture (i.e., direct- or gear-driven), bypass nozzle architecture (i.e., fixed- or variable geometry), and the high- and low-pressure compressor work split. Ramp weight, fuel burn, noise, and emissions are the parameters treated as dependent objective functions. These optimized solutions provide insight to the ultrahigh bypass engine design process and provide information to NASA program management to help guide its technology development efforts.

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.

Combining simulation and multi-objective optimisation for equipment quantity optimisation in container terminals

OpenAIRE

Lin, Zhougeng

2013-01-01

This thesis proposes a combination framework to integrate simulation and multi-objective optimisation (MOO) for container terminal equipment optimisation. It addresses how the strengths of simulation and multi-objective optimisation can be integrated to find high quality solutions for multiple objectives with low computational cost. Three structures for the combination framework are proposed respectively: pre-MOO structure, integrated MOO structure and post-MOO structure. The applications of ...

An Introduction to Object-Oriented Programming with a Didactic Microworld: "objectKarel"

Science.gov (United States)

Xinogalos, Stelios; Satratzemi, Maya; Dagdilelis, Vassilios

2006-01-01

The objects-first strategy to teaching programming has prevailed over the imperative-first and functional-first strategies during the last decade. However, the objects-first strategy has created added difficulties to both the teaching and learning of programming. In an attempt to confront these difficulties and support the objects-first strategy…

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

Using genetic algorithm to solve a new multi-period stochastic optimization model

Science.gov (United States)

Zhang, Xin-Li; Zhang, Ke-Cun

2009-09-01

This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.

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.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition †

Science.gov (United States)

Janko, Vito

2017-01-01

The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. PMID:29286301

Multi-objective optimization of a continuous thermally regenerative electrochemical cycle for waste heat recovery

International Nuclear Information System (INIS)

Long, Rui; Li, Baode; Liu, Zhichun; Liu, Wei

2015-01-01

An optimization analysis of a continuous TREC (thermally regenerative electrochemical cycle) was conducted with maximum power output and exergy efficiency as the objective functions simultaneously. For comparison, the power output, exergy efficiency, and thermal efficiency under the corresponding single-objective optimization schematics were also calculated. Under different optimization methods it was observed that the power output and the thermal efficiency increase with increasing inlet temperature of the heat source, whereas the exergy efficiency increases with increasing inlet temperature, reaches a maximum value, and then decreases. Results revealed that the optimal power output under the multi-objective optimization turned out to be slightly less than that obtained under the single-objective optimization for power output. However, the exergy and thermal efficiencies were much greater. Furthermore, the thermal exergy and exergy efficiency by single-objective optimization for energy efficiency shows no dominant advantage than that obtained under multi-objective optimization, comparing with the increase amplitude of the power output. This suggests that the multi-objective optimization could coordinate well both the power output and the exergy efficiency of the TREC system, and may serve as a more promising guide for operating and designing TREC systems. - Highlights: • An optimal analysis of a continuous TREC is conducted based on multi-objective optimization. • Performance under corresponding single-objective optimizations has also been calculated and compared. • Power under multi-objective optimization is slightly less than the maximum power. • Exergy and thermal efficiencies are much larger than that under the single-objective optimization.

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)

Evaluation of cephalogram using multi-objective frequency processing

Energy Technology Data Exchange (ETDEWEB)

Hagiwara, Sakae; Takizawa, Tsutomu; Osako, Miho; Kaneda, Takashi; Kasai, Kazutaka [Nihon Univ., Chiba (Japan). School of Dentistry at Matsudo

2002-12-01

A diagnosis with cephalogram is important for orthodontic treatment. Recently, computed radiography (CR) has been performed to the cephalogram. However, evaluation of multi-objective frequency processing (MFP) for cephalograms has been received little attention. The purpose of this study was to evaluate the cephalogram using MFP CR. At first, 450 lateral cephalograms were made, from 50 orthodontic patients, with 9 possible spatial frequency parameter combinations and a contrast scale held fixed in images processing. For each film, the clarity of radiographic images were estimated and scored with respect to landmark identification (total 26 points, 20 points of hard tissue and 6 points of soft tissue). A specific combination of spatial frequency scales (multi-frequency balance types (MRB) F-type, multi-frequency enhancement (MRE) 8) was proved to be adequate to achieve the optimal image quality in the cephalogram. (author)

Evaluation of cephalogram using multi-objective frequency processing

International Nuclear Information System (INIS)

Hagiwara, Sakae; Takizawa, Tsutomu; Osako, Miho; Kaneda, Takashi; Kasai, Kazutaka

2002-01-01

A diagnosis with cephalogram is important for orthodontic treatment. Recently, computed radiography (CR) has been performed to the cephalogram. However, evaluation of multi-objective frequency processing (MFP) for cephalograms has been received little attention. The purpose of this study was to evaluate the cephalogram using MFP CR. At first, 450 lateral cephalograms were made, from 50 orthodontic patients, with 9 possible spatial frequency parameter combinations and a contrast scale held fixed in images processing. For each film, the clarity of radiographic images were estimated and scored with respect to landmark identification (total 26 points, 20 points of hard tissue and 6 points of soft tissue). A specific combination of spatial frequency scales (multi-frequency balance types (MRB) F-type, multi-frequency enhancement (MRE) 8) was proved to be adequate to achieve the optimal image quality in the cephalogram. (author)

Multi-objective optimization in quantum parameter estimation

Science.gov (United States)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

Science.gov (United States)

Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza

2017-08-01

Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

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.

Genetic Network Programming with Reconstructed Individuals

Science.gov (United States)

Ye, Fengming; Mabu, Shingo; Wang, Lutao; Eto, Shinji; Hirasawa, Kotaro

A lot of research on evolutionary computation has been done and some significant classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), and Evolution Strategies (ES) have been studied. Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solution. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc., and GNP has obtained some outstanding results. Focusing on the GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP-RI). The aim of GNP-RI is to balance the exploitation and exploration of GNP, that is, to strengthen the exploitation ability by using the exploited information extensively during the evolution process of GNP and finally obtain better performances than that of GNP. In the proposed method, the worse individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worse individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving a good education. In this paper, GNP-RI is applied to the tile-world problem which is an excellent bench mark for evaluating the proposed architecture. The performance of GNP-RI is compared with that of the conventional GNP. The simulation results show some advantages of GNP-RI demonstrating its superiority over the conventional GNPs.

Developing robotic behavior using a genetic programming model

International Nuclear Information System (INIS)

Pryor, R.J.

1998-01-01

This report describes the methodology for using a genetic programming model to develop tracking behaviors for autonomous, microscale robotic vehicles. The use of such vehicles for surveillance and detection operations has become increasingly important in defense and humanitarian applications. Through an evolutionary process similar to that found in nature, the genetic programming model generates a computer program that when downloaded onto a robotic vehicle's on-board computer will guide the robot to successfully accomplish its task. Simulations of multiple robots engaged in problem-solving tasks have demonstrated cooperative behaviors. This report also discusses the behavior model produced by genetic programming and presents some results achieved during the study

Natural Gas Multi-Year Program Plan

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-12-01

This document comprises the Department of Energy (DOE) Natural Gas Multi-Year Program Plan, and is a follow-up to the `Natural Gas Strategic Plan and Program Crosscut Plans,` dated July 1995. DOE`s natural gas programs are aimed at simultaneously meeting our national energy needs, reducing oil imports, protecting our environment, and improving our economy. The Natural Gas Multi-Year Program Plan represents a Department-wide effort on expanded development and use of natural gas and defines Federal government and US industry roles in partnering to accomplish defined strategic goals. The four overarching goals of the Natural Gas Program are to: (1) foster development of advanced natural gas technologies, (2) encourage adoption of advanced natural gas technologies in new and existing markets, (3) support removal of policy impediments to natural gas use in new and existing markets, and (4) foster technologies and policies to maximize environmental benefits of natural gas use.

Multi-Model Estimation Based Moving Object Detection for Aerial Video

Directory of Open Access Journals (Sweden)

Yanning Zhang

2015-04-01

Full Text Available With the wide development of UAV (Unmanned Aerial Vehicle technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

Multi-physics and multi-objective design of heterogeneous SFR core: development of an optimization method under uncertainty

International Nuclear Information System (INIS)

Ammar, Karim

2014-01-01

Since Phenix shutting down in 2010, CEA does not have Sodium Fast Reactor (SFR) in operating condition. According to global energetic challenge and fast reactor abilities, CEA launched a program of industrial demonstrator called ASTRID (Advanced Sodium Technological Reactor for Industrial Demonstration), a reactor with electric power capacity equal to 600 MW. Objective of the prototype is, in first to be a response to environmental constraints, in second demonstrates the industrial viability of SFR reactor. The goal is to have a safety level at least equal to 3. generation reactors. ASTRID design integrates Fukushima feedback; Waste reprocessing (with minor actinide transmutation) and it linked industry. Installation safety is the priority. In all cases, no radionuclide should be released into environment. To achieve this objective, it is imperative to predict the impact of uncertainty sources on reactor behaviour. In this context, this thesis aims to develop new optimization methods for SFR cores. The goal is to improve the robustness and reliability of reactors in response to existing uncertainties. We will use ASTRID core as reference to estimate interest of new methods and tools developed. The impact of multi-Physics uncertainties in the calculation of the core performance and the use of optimization methods introduce new problems: How to optimize 'complex' cores (i.e. associated with design spaces of high dimensions with more than 20 variable parameters), taking into account the uncertainties? What is uncertainties behaviour for optimization core compare to reference core? Taking into account uncertainties, optimization core are they still competitive? Optimizations improvements are higher than uncertainty margins? The thesis helps to develop and implement methods necessary to take into account uncertainties in the new generation of simulation tools. Statistical methods to ensure consistency of complex multi-Physics simulation results are also

7 CFR 3550.51 - Program objectives.

Science.gov (United States)

2010-01-01

... 7 Agriculture 15 2010-01-01 2010-01-01 false Program objectives. 3550.51 Section 3550.51... AGRICULTURE DIRECT SINGLE FAMILY HOUSING LOANS AND GRANTS Section 502 Origination § 3550.51 Program objectives..., applicants are required to use section 502 funds in conjunction with funding or financing from other sources...

Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming

KAUST Repository

Kouramas, K.I.

2011-08-01

This work presents a new algorithm for solving the explicit/multi- parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step. © 2011 Elsevier Ltd. All rights reserved.

Shape optimization of high power centrifugal compressor using multi-objective optimal method

Energy Technology Data Exchange (ETDEWEB)

Kang, Hyun Soo; Lee, Jeong Min; Kim, Youn Jea [School of Mechanical Engineering, Sungkyunkwan University, Seoul (Korea, Republic of)

2015-03-15

In this study, a method for optimal design of impeller and diffuser blades in the centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was evaluated. A numerical simulation was conducted using ANSYS CFX with various values of impeller and diffuser parameters, which consist of leading edge (LE) angle, trailing edge (TE) angle, and blade thickness. Each of the parameters was divided into three levels. A total of 45 design points were planned using central composite design (CCD), which is one of the design of experiment (DOE) techniques. Response surfaces that were generated on the basis of the results of DOE were used to determine the optimal shape of impeller and diffuser blade. The entire process of optimization was conducted using ANSYS Design Xplorer (DX). Through the optimization, isentropic efficiency and pressure recovery coefficient, which are the main performance parameters of the centrifugal compressor, were increased by 0.3 and 5, respectively.

Shape optimization of high power centrifugal compressor using multi-objective optimal method

International Nuclear Information System (INIS)

Kang, Hyun Soo; Lee, Jeong Min; Kim, Youn Jea

2015-01-01

In this study, a method for optimal design of impeller and diffuser blades in the centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was evaluated. A numerical simulation was conducted using ANSYS CFX with various values of impeller and diffuser parameters, which consist of leading edge (LE) angle, trailing edge (TE) angle, and blade thickness. Each of the parameters was divided into three levels. A total of 45 design points were planned using central composite design (CCD), which is one of the design of experiment (DOE) techniques. Response surfaces that were generated on the basis of the results of DOE were used to determine the optimal shape of impeller and diffuser blade. The entire process of optimization was conducted using ANSYS Design Xplorer (DX). Through the optimization, isentropic efficiency and pressure recovery coefficient, which are the main performance parameters of the centrifugal compressor, were increased by 0.3 and 5, respectively

Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems