WorldWideScience

Sample records for hierarchical multiobjective optimization

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-04-15

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

  2. Ensemble-based hierarchical multi-objective production optimization of smart wells

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Van den Hof, P.M.J.; Jansen, J.D.

    2014-01-01

    In an earlier study two hierarchical multi-objective methods were suggested to include short-term targets in life-cycle production optimization. However this earlier study has two limitations: 1) the adjoint formulation is used to obtain gradient information, requiring simulator source code access a

  3. Ensemble-based hierarchical multi-objective production optimization of smart wells

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Hof, P.M.J. Van den; Jansen, J.D.

    2014-01-01

    In an earlier study, two hierarchical multiobjective methods were suggested to include short-term targets in life-cycle production optimization. However, this earlier study has two limitations: (1) the adjoint formulation is used to obtain gradient information, requiring simulator source code access

  4. Constrained Multiobjective Biogeography Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Hongwei Mo

    2014-01-01

    Full Text Available Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA.

  5. Constrained multiobjective biogeography optimization algorithm.

    Science.gov (United States)

    Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping

    2014-01-01

    Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA.

  6. Adaptive scalarization methods in multiobjective optimization

    CERN Document Server

    Eichfelder, Gabriele

    2008-01-01

    This book presents adaptive solution methods for multiobjective optimization problems based on parameter dependent scalarization approaches. Readers will benefit from the new adaptive methods and ideas for solving multiobjective optimization.

  7. A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT

    CERN Document Server

    Holdsworth, Clay; Liao, Jay; Phillips, Mark H

    2012-01-01

    Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then...

  8. 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...

  9. Multiobjective optimization in bioinformatics and computational biology.

    Science.gov (United States)

    Handl, Julia; Kell, Douglas B; Knowles, Joshua

    2007-01-01

    This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

  10. Multiobjective Topology Optimization of Energy Absorbing Materials

    Science.gov (United States)

    2015-08-01

    125–143 DOI 10.1007/s00158-014-1117-8 RESEARCH PAPER Multiobjective topology optimization of energy absorbing materials Raymond A. Wildman · George A...recent developments. J Multiscale Model 3(4):1–42 Qiao P, Yang M, Bobaru F (2008) Impact mechanics and high-energy absorbing materials: review . J Aerosp...ARL-RP-0533 ● AUG 2015 US Army Research Laboratory Multiobjective Topology Optimization of Energy Absorbing Materials by

  11. Genetic algorithms and fuzzy multiobjective optimization

    CERN Document Server

    Sakawa, Masatoshi

    2002-01-01

    Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...

  12. Combinatorial Multiobjective Optimization Using Genetic Algorithms

    Science.gov (United States)

    Crossley, William A.; Martin. Eric T.

    2002-01-01

    The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

  13. A Hierarchical Multiobjective Routing Model for MPLS Networks with Two Service Classes

    Science.gov (United States)

    Craveirinha, José; Girão-Silva, Rita; Clímaco, João; Martins, Lúcia

    This work presents a model for multiobjective routing in MPLS networks formulated within a hierarchical network-wide optimization framework, with two classes of services, namely QoS and Best Effort (BE) services. The routing model uses alternative routing and hierarchical optimization with two optimization levels, including fairness objectives. Another feature of the model is the use of an approximate stochastic representation of the traffic flows in the network, based on the concept of effective bandwidth. The theoretical foundations of a heuristic strategy for finding “good” compromise solutions to the very complex bi-level routing optimization problem, based on a conjecture concerning the definition of marginal implied costs for QoS flows and BE flows, will be described. The main features of a first version of this heuristic based on a bi-objective shortest path model and some preliminary results for a benchmark network will also be revealed.

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

    Institute of Scientific and Technical Information of China (English)

    曹瑞芬; 吴宜灿; 裴曦; 景佳; 李国丽; 程梦云; 李贵; 胡丽琴

    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 pl

  15. Overview of multi-objective optimization methods

    Institute of Scientific and Technical Information of China (English)

    雷秀娟; 史忠科

    2004-01-01

    To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.

  16. The Multiobjective Optimization of a Prismatic Drive

    CERN Document Server

    Bouyer, Emilie; Chablat, Damien; Angeles, Jorge

    2007-01-01

    The multiobjective optimization of Slide-o-Cam is reported in this paper. Slide-o-Cam is a cam mechanism with multiple rollers mounted on a common translating follower. This transmission provides pure-rolling motion, thereby reducing the friction of rack-and-pinions and linear drives. A Pareto frontier is obtained by means of multiobjective optimization. This optimization is based on three objective functions: (i) the pressure angle, which is a suitable performance index for the transmission because it determines the amount of force transmitted to the load vs. that transmitted to the machine frame; (ii) the Hertz pressure used to evaluate the stresses produced on the contact surface between cam and roller; and (iii) the size of the mechanism, characterized by the number of cams and their width.

  17. Novel multi-objective optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    Jie Zeng; Wei Nie

    2014-01-01

    Many multi-objective evolutionary algorithms (MOEAs) can converge to the Pareto optimal front and work wel on two or three objectives, but they deteriorate when faced with many-objective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values (instead of the Pareto-dominance relation to select candidate solutions), have been regarded as promising schemes that yield more satisfactory re-sults than wel-known algorithms, such as non-dominated sort-ing genetic algorithm (NSGA-II) and strength Pareto evolution-ary algorithm (SPEA2). However, they can suffer from having a slow convergence speed. This paper proposes a new indicator-based multi-objective optimization algorithm, namely, the multi-objective shuffled frog leaping algorithm based on the ε indicator (ε-MOSFLA). This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capa-bility of global search and quick convergence as an evolutionary strategy and a simple and effective ε-indicator as a fitness as-signment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show thatε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as wel as the speed of convergence.

  18. Waste Minimization Through Process Integration and Multi-objective Optimization

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    By avoiding or reducing the production of waste, waste minimization is an effective approach to solve the pollution problem in chemical industry. Process integration supported by multi-objective optimization provides a framework for process design or process retrofit by simultaneously optimizing on the aspects of environment and economics. Multi-objective genetic algorithm is applied in this area as the solution approach for the multi-objective optimization problem.

  19. Multiobjective Optimization and Phase Transitions

    CERN Document Server

    Seoane, Luís F

    2015-01-01

    Many complex systems obey to optimality conditions that are usually not simple. Conflicting traits often interact making a Multi Objective Optimization (MOO) approach necessary. Recent MOO research on complex systems report about the Pareto front (optimal designs implementing the best trade-off) in a qualitative manner. Meanwhile, research on traditional Simple Objective Optimization (SOO) often finds phase transitions and critical points. We summarize a robust framework that accounts for phase transitions located through SOO techniques and indicates what MOO features resolutely lead to phase transitions. These appear determined by the shape of the Pareto front, which at the same time is deeply related to the thermodynamic Gibbs surface. Indeed, thermodynamics can be written as an MOO from where its phase transitions can be parsimoniously derived; suggesting that the similarities between transitions in MOO-SOO and Statistical Mechanics go beyond mere coincidence.

  20. Flower pollination algorithm: A novel approach for multiobjective optimization

    Science.gov (United States)

    Yang, Xin-She; Karamanoglu, Mehmet; He, Xingshi

    2014-09-01

    Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.

  1. An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.

    Science.gov (United States)

    Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed

    2015-10-01

    Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front.

  2. Multiobjective Optimization Methodology A Jumping Gene Approach

    CERN Document Server

    Tang, KS

    2012-01-01

    Complex design problems are often governed by a number of performance merits. These markers gauge how good the design is going to be, but can conflict with the performance requirements that must be met. The challenge is reconciling these two requirements. This book introduces a newly developed jumping gene algorithm, designed to address the multi-functional objectives problem and supplies a viably adequate solution in speed. The text presents various multi-objective optimization techniques and provides the technical know-how for obtaining trade-off solutions between solution spread and converg

  3. Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2014-01-01

    Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

  4. APPLICATION OF FUZZY MATHEMATICS IN MULTI-OBJECTIVE OPTIMAL DESIGN

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    In order to overcome the problem that theoretical research lags behind practical application in the multi-objective optimal design,a practical method is suggested.In this method the fuzzy nearness is used to seek an overall solution of the multi-objective optimal design and analyse the features of the curved surface.The method is tested using three practical examples.

  5. An Algorithmic Framework for Multiobjective Optimization

    Science.gov (United States)

    Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

    2013-01-01

    Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

  6. An algorithmic framework for multiobjective optimization.

    Science.gov (United States)

    Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

    2013-01-01

    Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

  7. An Algorithmic Framework for Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    T. Ganesan

    2013-01-01

    Full Text Available Multiobjective (MO optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE, genetic algorithm (GA, gravitational search algorithm (GSA, and particle swarm optimization (PSO have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two. In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

  8. Controller tuning with evolutionary multiobjective optimization a holistic multiobjective optimization design procedure

    CERN Document Server

    Reynoso Meza, Gilberto; Sanchis Saez, Javier; Herrero Durá, Juan Manuel

    2017-01-01

    This book is devoted to Multiobjective Optimization Design (MOOD) procedures for controller tuning applications, by means of Evolutionary Multiobjective Optimization (EMO). It presents developments in tools, procedures and guidelines to facilitate this process, covering the three fundamental steps in the procedure: problem definition, optimization and decision-making. The book is divided into four parts. The first part, Fundamentals, focuses on the necessary theoretical background and provides specific tools for practitioners. The second part, Basics, examines a range of basic examples regarding the MOOD procedure for controller tuning, while the third part, Benchmarking, demonstrates how the MOOD procedure can be employed in several control engineering problems. The fourth part, Applications, is dedicated to implementing the MOOD procedure for controller tuning in real processes.

  9. Procedural Optimization Models for Multiobjective Flexible JSSP

    Directory of Open Access Journals (Sweden)

    Elena Simona NICOARA

    2013-01-01

    Full Text Available The most challenging issues related to manufacturing efficiency occur if the jobs to be sched-uled are structurally different, if these jobs allow flexible routings on the equipments and mul-tiple objectives are required. This framework, called Multi-objective Flexible Job Shop Scheduling Problems (MOFJSSP, applicable to many real processes, has been less reported in the literature than the JSSP framework, which has been extensively formalized, modeled and analyzed from many perspectives. The MOFJSSP lie, as many other NP-hard problems, in a tedious place where the vast optimization theory meets the real world context. The paper brings to discussion the most optimization models suited to MOFJSSP and analyzes in detail the genetic algorithms and agent-based models as the most appropriate procedural models.

  10. Hierarchical control based on Hopfield network for nonseparable optimization problems

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The nonseparable optimization control problem is considered, where the overall objective function is not of an additive form with respect to subsystems. Since there exists the problem that computation is very slow when using iterative algorithms in multiobjective optimization, Hopfield optimization hierarchical network based on IPM is presented to overcome such slow computation difficulty. Asymptotic stability of this Hopfield network is proved and its equilibrium point is the optimal point of the original problem. The simulation shows that the net is effective to deal with the optimization control problem for large-scale nonseparable steady state systems.

  11. Optimality Conditions in Nondifferentiable G-Invex Multiobjective Programming

    Directory of Open Access Journals (Sweden)

    Do Sang Kim

    2010-01-01

    Full Text Available We consider a class of nondifferentiable multiobjective programs with inequality and equality constraints in which each component of the objective function contains a term involving the support function of a compact convex set. We introduce G-Karush-Kuhn-Tucker conditions and G-Fritz John conditions for our nondifferentiable multiobjective programs. By using suitable G-invex functions, we establish G-Karush-Kuhn-Tucker necessary and sufficient optimality conditions, and G-Fritz John necessary and sufficient optimality conditions of our nondifferentiable multiobjective programs. Our optimality conditions generalize and improve the results in Antczak (2009 to the nondifferentiable case.

  12. Multi-objective optimization of steel nitriding

    Directory of Open Access Journals (Sweden)

    P. Cavaliere

    2016-03-01

    Full Text Available Steel nitriding is a thermo-chemical process largely employed in the machine components production to solve mainly wear and fatigue damage in materials. The process is strongly influenced by many different variables such as steel composition, nitrogen potential (range 0.8–35, temperature (range 350–1200 °C, time (range 2–180 hours. In the present study, the influence of such parameters affecting the nitriding layers' thickness, hardness, composition and residual stress was evaluated. The aim was to streamline the process by numerical–experimental analysis allowing to define the optimal conditions for the success of the process. The optimization software that was used is modeFRONTIER (Esteco, through which was defined a set of input parameters (steel composition, nitrogen potential, nitriding time, etc. evaluated on the basis of an optimization algorithm carefully chosen for the multi-objective analysis. The mechanical and microstructural results belonging to the nitriding process, performed with different processing conditions for various steels, are presented. The data were employed to obtain the analytical equations describing nitriding behavior as a function of nitriding parameters and steel composition. The obtained model was validated through control designs and optimized by taking into account physical and processing conditions.

  13. Optimal Multiobjective Design of Digital Filters Using Taguchi Optimization Technique

    Science.gov (United States)

    Ouadi, Abderrahmane; Bentarzi, Hamid; Recioui, Abdelmadjid

    2014-01-01

    The multiobjective design of digital filters using the powerful Taguchi optimization technique is considered in this paper. This relatively new optimization tool has been recently introduced to the field of engineering and is based on orthogonal arrays. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired frequency response while having minimum linear phase; hence, reducing the time response. The results demonstrate that the proposed problem solving approach blended with the use of the Taguchi optimization technique produced filters that fulfill the desired characteristics and are of practical use.

  14. Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks

    Directory of Open Access Journals (Sweden)

    Subbaraj Potti

    2011-01-01

    Full Text Available Problem statement: A new multi-objective approach, Strength Pareto Evolutionary Algorithm (SPEA, is presented in this paper to solve the shortest path routing problem. The routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: SPEA handles the shortest path routing problem as a true multi-objective optimization problem with competing and noncommensurable objectives. Results: SPEA combines several features of previous multi-objective evolutionary algorithms in a unique manner. SPEA stores nondominated solutions externally in another continuously-updated population and uses a hierarchical clustering algorithm to provide the decision maker with a manageable pareto-optimal set. SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. Conclusion: The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal nondominated solutions.

  15. Adaptive Multi-Objective Optimization Based on Feedback Design

    Institute of Scientific and Technical Information of China (English)

    窦立谦; 宗群; 吉月辉; 曾凡琳

    2010-01-01

    The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions.In this paper,a feedback structure for AMOO is designed.Moreover,the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions.Finally,the prop...

  16. A simulated annealing technique for multi-objective simulation optimization

    OpenAIRE

    Mahmoud H. Alrefaei; Diabat, Ali H.

    2009-01-01

    In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm ...

  17. Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

    Science.gov (United States)

    Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

    2015-06-18

    Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

  18. Multiobjective Optimization Based Vessel Collision Avoidance Strategy Optimization

    Directory of Open Access Journals (Sweden)

    Qingyang Xu

    2014-01-01

    Full Text Available The vessel collision accidents cause a great loss of lives and property. In order to reduce the human fault and greatly improve the safety of marine traffic, collision avoidance strategy optimization is proposed to achieve this. In the paper, a multiobjective optimization algorithm NSGA-II is adopted to search for the optimal collision avoidance strategy considering the safety as well as economy elements of collision avoidance. Ship domain and Arena are used to evaluate the collision risk in the simulation. Based on the optimization, an optimal rudder angle is recommended to navigator for collision avoidance. In the simulation example, a crossing encounter situation is simulated, and the NSGA-II searches for the optimal collision avoidance operation under the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS. The simulation studies exhibit the validity of the method.

  19. Tuning PID Controller Using Multiobjective Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Ibtissem Chiha

    2012-01-01

    Full Text Available This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp, Ki, and Kd by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

  20. Modeling and Multi-objective Optimization of Refinery Hydrogen Network

    Institute of Scientific and Technical Information of China (English)

    焦云强; 苏宏业; 廖祖维; 侯卫锋

    2011-01-01

    The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming (MINLP). A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.

  1. Balanced Combinations of Solutions in Multi-Objective Optimization

    CERN Document Server

    Glaßer, Christian; Witek, Maximilian

    2010-01-01

    For every list of integers x_1, ..., x_m there is some j such that x_1 + ... + x_j - x_{j+1} - ... - x_m \\approx 0. So the list can be nearly balanced and for this we only need one alternation between addition and subtraction. But what if the x_i are k-dimensional integer vectors? Using results from topological degree theory we show that balancing is still possible, now with k alternations. This result is useful in multi-objective optimization, as it allows a polynomial-time computable balance of two alternatives with conflicting costs. The application to two multi-objective optimization problems yields the following results: - A randomized 1/2-approximation for multi-objective maximum asymmetric traveling salesman, which improves and simplifies the best known approximation for this problem. - A deterministic 1/2-approximation for multi-objective maximum weighted satisfiability.

  2. Hybrid particle swarm optimization for multiobjective resource allocation

    Institute of Scientific and Technical Information of China (English)

    Yi Yang; Li Xiaoxing; Gu Chunqin

    2008-01-01

    Resource allocation (RA) is the problem of allocating resources among various artifacts or business units to meet one or more expected goals,such as maximizing the profits,minimizing the costs,or achieving the best qualities.A complex multiobjective RA is addressed,and a multiobjective mathematical model is used to find solutions efficiently.Then,an improved particle swarm algorithm (mO_PSO) is proposed combined with a new particle diversity controller policies and dissipation operation.Meanwhile,a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented.The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.

  3. 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...

  4. Entropy Diversity in Multi-Objective Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Eduardo J. Solteiro Pires

    2013-12-01

    Full Text Available Multi-objective particle swarm optimization (MOPSO is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.

  5. Global, Multi-Objective Trajectory Optimization With Parametric Spreading

    Science.gov (United States)

    Vavrina, Matthew A.; Englander, Jacob A.; Phillips, Sean M.; Hughes, Kyle M.

    2017-01-01

    Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented.

  6. 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 ...

  7. Multi-objective optimization of an axial compressor blade

    Energy Technology Data Exchange (ETDEWEB)

    Samad, Abdus; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of)

    2008-05-15

    Numerical optimization with multiple objectives is carried out for design of an axial compressor blade. Two conflicting objectives, total pressure ratio and adiabatic efficiency, are optimized with three design variables concerning sweep, lean and skew of blade stacking line. Single objective optimizations have been also performed. At the data points generated by D-optimal design, the objectives are calculated by three-dimensional Reynolds-averaged Navier-Stokes analysis. A second-order polynomial based response surface model is generated, and the optimal point is searched by sequential quadratic programming method for single objective optimization. Elitist non-dominated sorting of genetic algorithm (NSGA-II) with {epsilon}-constraint local search strategy is used for multi-objective optimization. Both objective function values are found to be improved as compared to the reference one by multi-objective optimization. The flow analysis results show the mechanism for the improvement of blade performance

  8. Multi-objective nested algorithms for optimal reservoir operation

    Science.gov (United States)

    Delipetrev, Blagoj; Solomatine, Dimitri

    2016-04-01

    The optimal reservoir operation is in general a multi-objective problem, meaning that multiple objectives are to be considered at the same time. For solving multi-objective optimization problems there exist a large number of optimization algorithms - which result in a generation of a Pareto set of optimal solutions (typically containing a large number of them), or more precisely, its approximation. At the same time, due to the complexity and computational costs of solving full-fledge multi-objective optimization problems some authors use a simplified approach which is generically called "scalarization". Scalarization transforms the multi-objective optimization problem to a single-objective optimization problem (or several of them), for example by (a) single objective aggregated weighted functions, or (b) formulating some objectives as constraints. We are using the approach (a). A user can decide how many multi-objective single search solutions will generate, depending on the practical problem at hand and by choosing a particular number of the weight vectors that are used to weigh the objectives. It is not guaranteed that these solutions are Pareto optimal, but they can be treated as a reasonably good and practically useful approximation of a Pareto set, albeit small. It has to be mentioned that the weighted-sum approach has its known shortcomings because the linear scalar weights will fail to find Pareto-optimal policies that lie in the concave region of the Pareto front. In this context the considered approach is implemented as follows: there are m sets of weights {w1i, …wni} (i starts from 1 to m), and n objectives applied to single objective aggregated weighted sum functions of nested dynamic programming (nDP), nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL). By employing the multi-objective optimization by a sequence of single-objective optimization searches approach, these algorithms acquire the multi-objective properties

  9. Model-based multiobjective evolutionary algorithm optimization for HCCI engines

    OpenAIRE

    Ma, He; Xu, Hongming; Wang, Jihong; Schnier, Thorsten; Neaves, Ben; Tan, Cheng; Wang, Zhi

    2014-01-01

    Modern engines feature a considerable number of adjustable control parameters. With this increasing number of Degrees of Freedom (DoF) for engines, and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated engine optimization approach is desired. In this paper, a self-learning evolutionary algorithm based multi-objective globally optimization approach for a H...

  10. A Multiobjective Optimization Model in Automotive Supply Chain Networks

    Directory of Open Access Journals (Sweden)

    Abdolhossein Sadrnia

    2013-01-01

    Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.

  11. Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

    Science.gov (United States)

    Dokeroglu, Tansel; Sert, Seyyit Alper; Cinar, Muhammet Serkan

    2014-01-01

    With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose. PMID:24892048

  12. Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

    Directory of Open Access Journals (Sweden)

    Tansel Dokeroglu

    2014-01-01

    Full Text Available With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose.

  13. Multi-objective optimization approach for air traffic flow management

    Directory of Open Access Journals (Sweden)

    Fadil Rabie

    2017-01-01

    The decision-making stage was then performed with the aid of data clustering techniques to reduce the sizeof the Pareto-optimal set and obtain a smaller representation of the multi-objective design space, there by making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

  14. Multi-objective quantum genetic algorithm in WSNs distribution optimization

    Science.gov (United States)

    Wen, Hao; Ren, Hong-liang

    2013-03-01

    To achieve lower energy and higher detection coverage simultaneously in scattering distribution wireless sensor networks (WSNs), a multi-objective optimization function combined with area coverage and node-communication energy is constructed. Based on the multi-objective quantum genetic algorithm (Mo-QGA) proposed by Li Bin and Zhuang-zhen Quan et al, we have obtained optimum solutions close to Pareto front. Experimental results indicate that the Mo-QGA has advantages both on efficiency and coverage, as well as low energy.

  15. Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    P. Sabarinath

    2015-01-01

    Full Text Available The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

  16. Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

    Science.gov (United States)

    Peng, Haijun; Wang, Wei

    2016-10-01

    An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

  17. Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

    Institute of Scientific and Technical Information of China (English)

    GONG MaoGuo; JIAO LiCheng; MA WenPing; DU HaiFeng

    2008-01-01

    Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affin-ity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the sub-dominant antibodies, while the cryptic antibodies are redundant and have no func-tion during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-II, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

  18. Covariance matrix adaptation for multi-objective optimization.

    Science.gov (United States)

    Igel, Christian; Hansen, Nikolaus; Roth, Stefan

    2007-01-01

    The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compared to the standard CMA-ES. The elitist CMA-ES turns out to be slightly faster on unimodal functions, but is more prone to getting stuck in sub-optimal local minima. In the new multi-objective CMAES (MO-CMA-ES) a population of individuals that adapt their search strategy as in the elitist CMA-ES is maintained. These are subject to multi-objective selection. The selection is based on non-dominated sorting using either the crowding-distance or the contributing hypervolume as second sorting criterion. Both the elitist single-objective CMA-ES and the MO-CMA-ES inherit important invariance properties, in particular invariance against rotation of the search space, from the original CMA-ES. The benefits of the new MO-CMA-ES in comparison to the well-known NSGA-II and to NSDE, a multi-objective differential evolution algorithm, are experimentally shown.

  19. Hierarchical satisfying optimal algorithm with different importance and priorities

    Institute of Scientific and Technical Information of China (English)

    Li Shaoyuan; Teng Changjun

    2005-01-01

    A hierarchical satisfying optimal algorithm incorporating different importance and preemptive priorities is formulated. With the priority structure given by the decision-maker in the constrained multi-objective multi-degree-of-freedom optimization (CMMO) problem, the commonly used quadratic programming model is converted into a two-level optimization problem solved by the tolerant lexicographic method and the varying-domain optimization method. In contrast to previous works, the proposed approach allows the decision-maker to determine a desirable achievement degree for each goal to reflect explicitly the relative importance of these goals. The resulting solutions satisfy both the preemptive priority structure and have the maximum achievement degrees in sum. The power of the proposed approach is demonstrated with an example.

  20. MULTIOBJECT OPTIMIZATION OF A CENTRIFUGAL IMPELLER USING EVOLUTIONARY ALGORITHMS

    Institute of Scientific and Technical Information of China (English)

    Li Jun; Liu Lijun; Feng Zhenping

    2004-01-01

    Application of the multiobjective evolutionary algorithms to the aerodynamic optimization design of a centrifugal impeller is presented. The aerodynamic performance of a centrifugal impeller is evaluated by using the three-dimensional Navier-Stokes solutions. The typical centrifugal impeller is redesigned for maximization of the pressure rise and blade load and minimization of the rotational total pressure loss at the given flow conditions. The B閦ier curves are used to parameterize the three-dimensional impeller blade shape. The present method obtains many reasonable Pareto optimal designs that outperform the original centrifugal impeller. Detailed observation of the certain Pareto optimal design demonstrates the feasibility of the present multiobjective optimization method tool for turbomachinery design.

  1. Multiobjective hyper heuristic scheme for system design and optimization

    Science.gov (United States)

    Rafique, Amer Farhan

    2012-11-01

    As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

  2. Thermoeconomic Analysis and Multiobjective Optimization of a Solar Desalination Plant

    Directory of Open Access Journals (Sweden)

    Hamid Mokhtari

    2014-01-01

    Full Text Available A solar desalination plant consisting of solar parabolic collectors, steam generators, and MED unit was simulated technoeconomically and optimized using multiobjective genetic algorithm. A simulation code was developed using MATLAB language programming. Indirect steam generation using different thermal oils including THERMINOL VP1, THERMINOL66, and THERMINOL59 was also investigated. Objective function consisted of 17 essential parameters such as diameter of heat collector element, collector width, steam generator pinch, approach temperatures, and MED number of effects. Simulation results showed that THERMINOL VP1 had superior properties and produced more desalinated water than other heat transfer fluids. Performance of the plant was analyzed on four characteristic days of the year to show that multiobjective optimization technique can be used to obtain an optimized solution, in which the product flow rate increased, while total investment and O&M costs decreased compared to the base case.

  3. Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Ya-zhong Luo

    2014-01-01

    Full Text Available A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

  4. Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

    Science.gov (United States)

    Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

    2016-12-01

    Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

  5. Multiobjective muffler shape optimization with hybrid acoustics modeling.

    Science.gov (United States)

    Airaksinen, Tuomas; Heikkola, Erkki

    2011-09-01

    This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.

  6. A Multiobjective Optimization Framework for Stochastic Control of Complex Systems

    Energy Technology Data Exchange (ETDEWEB)

    Malikopoulos, Andreas [ORNL; Maroulas, Vasileios [ORNL; Xiong, Professor Jie [The University of Tennessee

    2015-01-01

    This paper addresses the problem of minimizing the long-run expected average cost of a complex system consisting of subsystems that interact with each other and the environment. We treat the stochastic control problem as a multiobjective optimization problem of the one-stage expected costs of the subsystems, and we show that the control policy yielding the Pareto optimal solution is an optimal control policy that minimizes the average cost criterion for the entire system. For practical situations with constraints consistent to those we study here, our results imply that the Pareto control policy may be of value in deriving online an optimal control policy in complex systems.

  7. Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm

    Science.gov (United States)

    Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.

    2017-02-01

    Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.

  8. Image meshing via hierarchical optimization

    Institute of Scientific and Technical Information of China (English)

    Hao XIE; Ruo-feng TONG‡

    2016-01-01

    Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., defi nition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to fi nd a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to fi nd a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to fi ner ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

  9. Multiobjective Optimization Problem of Multireservoir System in Semiarid Areas

    Directory of Open Access Journals (Sweden)

    Z. J. Chen

    2013-01-01

    Full Text Available With the increasing scarcity of water resources, the growing importance of the optimization operation of the multireservoir system in water resources development, utilization, and management is increasingly evident. Some of the existing optimization methods are inadequate in applicability and effectiveness. Therefore, we need further research in how to enhance the applicability and effectiveness of the algorithm. On the basis of the research of the multireservoir system’s operating parameters in the Urumqi River basin, we establish a multiobjective optimization problem (MOP model of water resources development, which meets the requirements of water resources development. In the mathematical model, the domestic water consumption is the biggest, the production of industry and agricultural is the largest, the gross output value of industry and agricultural is the highest, and the investment of the water development is the minimum. We use the weighted variable-step shuffled frog leaping algorithm (SFLA to resolve it, which satisfies the constraints. Through establishing the test function and performance metrics, we deduce the evolutionary algorithms, which suit for solving MOP of the scheduling, and realize the multiobjective optimization of the multireservoir system. After that, using the fuzzy theory, we convert the competitive multiobjective function into single objective problem of maximum satisfaction, which is the only solution. A feasible solution is provided to resolve the multiobjective scheduling optimization of multireservoir system in the Urumqi River basin. It is the significance of the layout of production, the regional protection of ecological environment, and the sufficient and rational use of natural resources, in Urumqi and the surrounding areas.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    Dan WU; Feng-ping WU; Yan-ping CHEN

    2009-01-01

    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.

  12. Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI.

    Science.gov (United States)

    Cerrolaza, Juan J; Villanueva, Arantxa; Cabeza, Rafael

    2012-03-01

    The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.

  13. Novel electromagnetism-like mechanism method for multiobjective optimization problems

    Institute of Scientific and Technical Information of China (English)

    Lixia Han,Shujuan Jiang,; Shaojiang Lan

    2015-01-01

    As a new-style stochastic algorithm, the electro-magnetism-like mechanism (EM) method gains more and more attention from many researchers in recent years. A novel model based on EM (NMEM) for multiobjective optimization problems is proposed, which regards the charge of al particles as the con-straints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the pro-posed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.

  14. The Multiobjective Trajectory Optimization for Hypersonic Glide Vehicle Based on Normal Boundary Intersection Method

    OpenAIRE

    Zhengnan Li; Tao Yang; Zhiwei Feng

    2016-01-01

    To solve the multiobjective optimization problem on hypersonic glider vehicle trajectory design subjected to complex constraints, this paper proposes a multiobjective trajectory optimization method that combines the boundary intersection method and pseudospectral method. The multiobjective trajectory optimization problem (MTOP) is established based on the analysis of the feature of hypersonic glider vehicle trajectory. The MTOP is translated into a set of general optimization subproblems by u...

  15. Tradeoff Analysis for Optimal Multiobjective Inventory Model

    Directory of Open Access Journals (Sweden)

    Longsheng Cheng

    2013-01-01

    Full Text Available Deterministic inventory model, the economic order quantity (EOQ, reveals that carrying inventory or ordering frequency follows a relation of tradeoff. For probabilistic demand, the tradeoff surface among annual order, expected inventory and shortage are useful because they quantify what the firm must pay in terms of ordering workload and inventory investment to meet the customer service desired. Based on a triobjective inventory model, this paper employs the successive approximation to obtain efficient control policies outlining tradeoffs among conflicting objectives. The nondominated solutions obtained by successive approximation are further used to plot a 3D scatterplot for exploring the relationships between objectives. Visualization of the tradeoffs displayed by the scatterplots justifies the computation effort done in the experiment, although several iterations needed to reach a nondominated solution make the solution procedure lengthy and tedious. Information elicited from the inverse relationships may help managers make deliberate inventory decisions. For the future work, developing an efficient and effective solution procedure for tradeoff analysis in multiobjective inventory management seems imperative.

  16. HIERARCHICAL OPTIMIZATION MODEL ON GEONETWORK

    Directory of Open Access Journals (Sweden)

    Z. Zha

    2012-07-01

    Full Text Available In existing construction experience of Spatial Data Infrastructure (SDI, GeoNetwork, as the geographical information integrated solution, is an effective way of building SDI. During GeoNetwork serving as an internet application, several shortcomings are exposed. The first one is that the time consuming of data loading has been considerately increasing with the growth of metadata count. Consequently, the efficiency of query and search service becomes lower. Another problem is that stability and robustness are both ruined since huge amount of metadata. The final flaw is that the requirements of multi-user concurrent accessing based on massive data are not effectively satisfied on the internet. A novel approach, Hierarchical Optimization Model (HOM, is presented to solve the incapability of GeoNetwork working with massive data in this paper. HOM optimizes the GeoNetwork from these aspects: internal procedure, external deployment strategies, etc. This model builds an efficient index for accessing huge metadata and supporting concurrent processes. In this way, the services based on GeoNetwork can maintain stable while running massive metadata. As an experiment, we deployed more than 30 GeoNetwork nodes, and harvest nearly 1.1 million metadata. From the contrast between the HOM-improved software and the original one, the model makes indexing and retrieval processes more quickly and keeps the speed stable on metadata amount increasing. It also shows stable on multi-user concurrent accessing to system services, the experiment achieved good results and proved that our optimization model is efficient and reliable.

  17. Replication in Overlay Networks: A Multi-objective Optimization Approach

    Science.gov (United States)

    Al-Haj Hassan, Osama; Ramaswamy, Lakshmish; Miller, John; Rasheed, Khaled; Canfield, E. Rodney

    Recently, overlay network-based collaborative applications such as instant messaging, content sharing, and Internet telephony are becoming increasingly popular. Many of these applications rely upon data-replication to achieve better performance, scalability, and reliability. However, replication entails various costs such as storage for holding replicas and communication overheads for ensuring replica consistency. While simple rule-of-thumb strategies are popular for managing the cost-benefit tradeoffs of replication, they cannot ensure optimal resource utilization. This paper explores a multi-objective optimization approach for replica management, which is unique in the sense that we view the various factors influencing replication decisions such as access latency, storage costs, and data availability as objectives, and not as constraints. This enables us to search for solutions that yield close to optimal values for these parameters. We propose two novel algorithms, namely multi-objective Evolutionary (MOE) algorithm and multi-objective Randomized Greedy (MORG) algorithm for deciding the number of replicas as well as their placement within the overlay. While MOE yields higher quality solutions, MORG is better in terms of computational efficiency. The paper reports a series of experiments that demonstrate the effectiveness of the proposed algorithms.

  18. Cross Entropy multiobjective optimization for water distribution systems design

    Science.gov (United States)

    Perelman, Lina; Ostfeld, Avi; Salomons, Elad

    2008-09-01

    A methodology extending the Cross Entropy combinatorial optimization method originating from an adaptive algorithm for rare events simulation estimation, to multiobjective optimization of water distribution systems design is developed and demonstrated. The single objective optimal design problem of a water distribution system is commonly to find the water distribution system component characteristics that minimize the system capital and operational costs such that the system hydraulics is maintained and constraints on quantities and pressures at the consumer nodes are fulfilled. The multiobjective design goals considered herein are the minimization of the network capital and operational costs versus the minimization of the maximum pressure deficit of the network demand nodes. The proposed methodology is demonstrated using two sample applications from the research literature and is compared to the NSGA-II multiobjective scheme. The method was found to be robust in that it produced very similar Pareto fronts in almost all runs. The suggested methodology provided improved results in all trails compared to the NSGA-II algorithm.

  19. MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II

    Energy Technology Data Exchange (ETDEWEB)

    Yang, L.; Li, Y.; Guo, W.; Krinsky, S.

    2011-03-28

    In this paper we present a multiobjective approach to the dynamic aperture (DA) optimization. Taking the NSLS-II lattice as an example, we have used both sextupoles and quadrupoles as tuning variables to optimize both on-momentum and off-momentum DA. The geometric and chromatic sextupoles are used for nonlinear properties while the tunes are independently varied by quadrupoles. The dispersion and emittance are fixed during tunes variation. The algorithms, procedures, performances and results of our optimization of DA will be discussed and they are found to be robust, general and easy to apply to similar problems.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    João Soares

    2016-10-01

    Full Text Available 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-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus. It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs, several distributed generation (DG, customers with demand response (DR contracts and energy storage systems (ESS. The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.

  2. Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Xiyang Liu

    2016-01-01

    Full Text Available Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

  3. Study of Multi-objective Fuzzy Optimization for Path Planning

    Institute of Scientific and Technical Information of China (English)

    WANG Yanyang; WEI Tietao; QU Xiangju

    2012-01-01

    During path planning,it is necessary to satisfy the requirements of multiple objectives.Multi-objective synthesis is based on the need of flight mission and subjectivity inclination of decision-maker.The decision-maker,however,has illegibility for understanding the requirements of multiple objectives and the subjectivity inclination.It is important to develop a reasonable cost performance index for describing the illegibility of the decision-maker in multi-objective path planning.Based on Voronoi diagram method for the path planning,this paper studies the synthesis method of the multi-objective cost performance index.According to the application of the cost performance index to the path planning based on Voronoi diagram method,this paper analyzes the cost performance index which has been referred to at present.The analysis shows the insufficiency of the cost performance index at present,i.e.,it is difficult to synthesize sub-objective functions because of the great disparity of the sub-objective functions.Thus,a new approach is developed to optimize the cost performance index with the multi-objective fuzzy optimization strategy,and an improved performance index is established,which could coordinate the weight conflict of the sub-objective functions.Finally,the experimental result shows the effectiveness of the proposed approach.

  4. Utopian preference mapping and the utopian preference method for group multiobjective optimization

    Institute of Scientific and Technical Information of China (English)

    HU Yuda; HONG Zhenjie; ZHOU Xuanwei

    2003-01-01

    The individual utopian preference and the group utopian preference on a set of alternatives, and the concept of the utopian preference mapping from the individual utopian preferences, to the group utopian preference, based on the utopian points of the corresponding multiobjective optimization models proposed by decision makers are introduced. Through studying the various fundamental properties of the utopian preference mapping, a method for solving group multiobjective optimization problems with multiple multiobjective optimization models is constructed.

  5. Multi-objective optimization of acoustic black hole vibration absorbers.

    Science.gov (United States)

    Shepherd, Micah R; Feurtado, Philip A; Conlon, Stephen C

    2016-09-01

    Structures with power law tapers exhibit the acoustic black hole (ABH) effect and can be used for vibration reduction. However, the design of ABHs for vibration reduction requires consideration of the underlying theory and its regions of validity. To address the competing nature of the best ABH design for vibration reduction and the underlying theoretical assumptions, a multi-objective approach is used to find the lowest frequency where both criteria are sufficiently met. The Pareto optimality curve is estimated for a range of ABH design parameters. The optimal set could then be used to implement an ABH vibration absorber.

  6. Multi-objective genetic optimization of linear construction projects

    Directory of Open Access Journals (Sweden)

    Fatma A. Agrama

    2012-08-01

    Full Text Available In the real world, the majority cases of optimization problems, met by engineers, are composed of several conflicting objectives. This paper presents an approach for a multi-objective optimization model for scheduling linear construction projects. Linear construction projects have many identical units wherein activities repeat from one unit to another. Highway, pipeline, and tunnels are good examples that exhibit repetitive characteristics. These projects represent a large portion of the construction industry. The present model enables construction planners to generate optimal/near-optimal construction plans that minimize project duration, total work interruptions, and total number of crews. Each of these plans identifies, from a set of feasible alternatives, optimal crew synchronization for each activity and activity interruptions at each unit. This model satisfies the following aspects: (1 it is based on the line of balance technique; (2 it considers non-serial typical activities networks with finish–start relationship and both lag or overlap time between activities is allowed; (3 it utilizes a multi-objective genetic algorithms approach; (4 it is developed as a spreadsheet template that is easy to use. Details of the model with visual charts are presented. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the scheduling of linear construction projects.

  7. Estimation of subsurface geomodels by multi-objective stochastic optimization

    Science.gov (United States)

    Emami Niri, Mohammad; Lumley, David E.

    2016-06-01

    We present a new method to estimate subsurface geomodels using a multi-objective stochastic search technique that allows a variety of direct and indirect measurements to simultaneously constrain the earth model. Inherent uncertainties and noise in real data measurements may result in conflicting geological and geophysical datasets for a given area; a realistic earth model can then only be produced by combining the datasets in a defined optimal manner. One approach to solving this problem is by joint inversion of the various geological and/or geophysical datasets, and estimating an optimal model by optimizing a weighted linear combination of several separate objective functions which compare simulated and observed datasets. In the present work, we consider the joint inversion of multiple datasets for geomodel estimation, as a multi-objective optimization problem in which separate objective functions for each subset of the observed data are defined, followed by an unweighted simultaneous stochastic optimization to find the set of best compromise model solutions that fits the defined objectives, along the so-called "Pareto front". We demonstrate that geostatistically constrained initializations of the algorithm improves convergence speed and produces superior geomodel solutions. We apply our method to a 3D reservoir lithofacies model estimation problem which is constrained by a set of geological and geophysical data measurements and attributes, and assess the sensitivity of the resulting geomodels to changes in the parameters of the stochastic optimization algorithm and the presence of realistic seismic noise conditions.

  8. Fuzzy Multiobjective Traffic Light Signal Optimization

    Directory of Open Access Journals (Sweden)

    N. Shahsavari Pour

    2013-01-01

    Full Text Available Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the control of fluctuating traffic volumes such as oversaturated or unusual load conditions. The problem is solved by genetic algorithm, and a new defuzzification method is introduced. The performance of the new defuzzification method (NDM is compared with the centroid point defuzzification method (CPDM by using ANOVA. Finally, an illustrative example is presented to show the competency of proposed algorithm.

  9. Angelic Hierarchical Planning: Optimal and Online Algorithms

    Science.gov (United States)

    2008-12-06

    restrict our attention to plans in I∗(Act, s0). Definition 2. ( Parr and Russell , 1998) A plan ah∗ is hierarchically optimal iff ah∗ = argmina∈I∗(Act,s0):T...Murdock, Dan Wu, and Fusun Yaman. SHOP2: An HTN planning system. JAIR, 20:379–404, 2003. Ronald Parr and Stuart Russell . Reinforcement Learning with...Angelic Hierarchical Planning: Optimal and Online Algorithms Bhaskara Marthi Stuart J. Russell Jason Wolfe Electrical Engineering and Computer

  10. A synthetic approach to multiobjective optimization

    CERN Document Server

    Lovison, Alberto

    2010-01-01

    We propose a strategy for approximating Pareto optimal sets based on the global analysis framework proposed by Smale (Dynamical systems, Academic Press, New York (1973) 531--544). We speak about \\emph{synthetic} approach because the optimal set is natively approximated by means of a compound geometrical object, i.e., a simplicial complex, rather than by an unstructured scatter of individual optima. The method distinguishes the hierarchy between singular set, Pareto critical set and stable Pareto critical set. Furthermore, a quadratic convergence result in set wise sense is proven and tested over numerical examples.

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

    Science.gov (United States)

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

    2017-07-01

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

  12. Multi-objective optimization of aerostructures inspired by nature

    Science.gov (United States)

    Kearney, Adam C.

    The focus of this doctoral work is on the optimization of aircraft wing structures. The optimization was performed against the shape, size and topology of simple aircraft wing designs. A simple morphing wing actuator optimization is performed as well as a wing panel buckling topology optimization. This is done with biologically-inspired mathematical systems including a map L-system, a multi-objective genetic algorithm, and cellular structures represented by Voronoi diagrams. As with most aircraft optimizations, both studies aim to minimize the total weight of a wing while simultaneously meeting stiffness and strength requirements. Optimization is performed with the scripts developed in MATLAB as well as through the use of finite element codes, NASTRAN and LS-Dyna. The intent of this methodology is to develop unique designs inspired by nature and optimized through natural selection. The optimal designs are those with minimal weight as well as additional requirements specific to the problems. The designs and methodology have the potential to be of use in determining minimum weight designs in aircraft structures. A literature review of optimization techniques, methodology and method validation, and optimization comparisons is presented. The buckling panel optimization considered here also includes composite buckling failure and manufacturing assumptions for composite panels. The panels are optimized for mass and strength by controlling the laminate stacking sequence, stiffener size, and topology. The morphing wing is optimized for actuator loading and redundancy.

  13. Modeling urban air pollution with optimized hierarchical fuzzy inference system.

    Science.gov (United States)

    Tashayo, Behnam; Alimohammadi, Abbas

    2016-10-01

    Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.

  14. PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning.

    Science.gov (United States)

    Fiege, Jason; McCurdy, Boyd; Potrebko, Peter; Champion, Heather; Cull, Andrew

    2011-09-01

    In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows promise in optimizing the number

  15. Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

    Science.gov (United States)

    Holst, Terry L.

    2005-01-01

    A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

  16. MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

    Science.gov (United States)

    A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

  17. Evolutionary Multi-objective Portfolio Optimization in Practical Context

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former,this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library,demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.

  18. A Novel Multiobjective Optimization Method Based on Sensitivity Analysis

    Directory of Open Access Journals (Sweden)

    Tiane Li

    2016-01-01

    Full Text Available For multiobjective optimization problems, different optimization variables have different influences on objectives, which implies that attention should be paid to the variables according to their sensitivity. However, previous optimization studies have not considered the variables sensitivity or conducted sensitivity analysis independent of optimization. In this paper, an integrated algorithm is proposed, which combines the optimization method SPEA (Strength Pareto Evolutionary Algorithm with the sensitivity analysis method SRCC (Spearman Rank Correlation Coefficient. In the proposed algorithm, the optimization variables are worked as samples of sensitivity analysis, and the consequent sensitivity result is used to guide the optimization process by changing the evolutionary parameters. Three cases including a mathematical problem, an airship envelope optimization, and a truss topology optimization are used to demonstrate the computational efficiency of the integrated algorithm. The results showed that this algorithm is able to simultaneously achieve parameter sensitivity and a well-distributed Pareto optimal set, without increasing the computational time greatly in comparison with the SPEA method.

  19. A Multiobjective Optimization Algorithm Based on Discrete Bacterial Colony Chemotaxis

    Directory of Open Access Journals (Sweden)

    Zhigang Lu

    2014-01-01

    Full Text Available Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.

  20. Well Field Management Using Multi-Objective Optimization

    DEFF Research Database (Denmark)

    Hansen, Annette Kirstine; Hendricks Franssen, H. J.; Bauer-Gottwein, Peter

    2013-01-01

    Efficient management of groundwater resources is important because groundwater availability is limited and, locally, groundwater quality has been impaired because of contamination. Here we present a multi-objective optimization framework for improving the management of a water works that operates...... with infiltration basins, injection wells and abstraction wells. The two management objectives are to minimize the amount of water needed for infiltration and to minimize the risk of getting contaminated water into the drinking water wells. The management is subject to a daily demand fulfilment constraint. Two...... optimization results are presented for the Hardhof water works in Zurich, Switzerland. It is found that both methods perform better than the historical management. The constant scheduling performs best in fairly stable conditions, whereas the sequential optimization performs best in extreme situations...

  1. Pricing Resources in LTE Networks through Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Yung-Liang Lai

    2014-01-01

    Full Text Available The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1 maximizing operator profit and (2 maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.

  2. Pricing resources in LTE networks through multiobjective optimization.

    Science.gov (United States)

    Lai, Yung-Liang; Jiang, Jehn-Ruey

    2014-01-01

    The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.

  3. Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization

    Science.gov (United States)

    Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)

    2002-01-01

    We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.

  4. Effective multi-objective optimization with the coral reefs optimization algorithm

    Science.gov (United States)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Portilla-Figueras, J. A.; Prieto, L.

    2016-06-01

    In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective Coral Reefs Optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.

  5. Multipurpose Water Reservoir Management: An Evolutionary Multiobjective Optimization Approach

    Directory of Open Access Journals (Sweden)

    Luís A. Scola

    2014-01-01

    Full Text Available The reservoirs that feed large hydropower plants should be managed in order to provide other uses for the water resources. Those uses include, for instance, flood control and avoidance, irrigation, navigability in the rivers, and other ones. This work presents an evolutionary multiobjective optimization approach for the study of multiple water usages in multiple interlinked reservoirs, including both power generation objectives and other objectives not related to energy generation. The classical evolutionary algorithm NSGA-II is employed as the basic multiobjective optimization machinery, being modified in order to cope with specific problem features. The case studies, which include the analysis of a problem which involves an objective of navigability on the river, are tailored in order to illustrate the usefulness of the data generated by the proposed methodology for decision-making on the problem of operation planning of multiple reservoirs with multiple usages. It is shown that it is even possible to use the generated data in order to determine the cost of any new usage of the water, in terms of the opportunity cost that can be measured on the revenues related to electric energy sales.

  6. Fuzzy Preference Incorporated Evolutionary Algorithm for Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Surafel Luleseged Tilahun

    2011-01-01

    Full Text Available Multiobjective evolutionary method is a way to overcome the limitation of the classical methods, by finding multiple solutions within a single run of the solution procedure. The aim of having a solution method for multiobjective optimization problem is to help the decision maker in getting the best solution. Usually the decision maker is not interested in a diverse set of Pareto optimal points. So, it is necessary to incorporate the decision maker’s preference so that the algorithm gives out alternative solutions around the decision maker’s preference. The problem in incorporating the decision maker’s preference is that the decision maker may not have a solid guide line in comparing tradeoffs of objectives. However, it is easy for the decision maker to compare in a fuzzy way. This paper discusses on incorporating a fuzzy tradeoffs in the evolutionary algorithm to zoom out the region where the decision maker’s preference lies. By using test functions it has shown that it is possible to give points in the region on the Pareto front where the decision maker’s interest lies.

  7. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Na Tian

    2015-01-01

    Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

  8. Advances in aircraft design: Multiobjective optimization and a markup language

    Science.gov (United States)

    Deshpande, Shubhangi

    Today's modern aerospace systems exhibit strong interdisciplinary coupling and require a multidisciplinary, collaborative approach. Analysis methods that were once considered feasible only for advanced and detailed design are now available and even practical at the conceptual design stage. This changing philosophy for conducting conceptual design poses additional challenges beyond those encountered in a low fidelity design of aircraft. This thesis takes some steps towards bridging the gaps in existing technologies and advancing the state-of-the-art in aircraft design. The first part of the thesis proposes a new Pareto front approximation method for multiobjective optimization problems. The method employs a hybrid optimization approach using two derivative free direct search techniques, and is intended for solving blackbox simulation based multiobjective optimization problems with possibly nonsmooth functions where the analytical formof the objectives is not known and/or the evaluation of the objective function(s) is very expensive (very common in multidisciplinary design optimization). A new adaptive weighting scheme is proposed to convert a multiobjective optimization problem to a single objective optimization problem. Results show that the method achieves an arbitrarily close approximation to the Pareto front with a good collection of well-distributed nondominated points. The second part deals with the interdisciplinary data communication issues involved in a collaborative mutidisciplinary aircraft design environment. Efficient transfer, sharing, and manipulation of design and analysis data in a collaborative environment demands a formal structured representation of data. XML, a W3C recommendation, is one such standard concomitant with a number of powerful capabilities that alleviate interoperability issues. A compact, generic, and comprehensive XML schema for an aircraft design markup language (ADML) is proposed here to provide a common language for data

  9. 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.

  10. Design of an operational transconductance amplifier applying multiobjective optimization techniques

    Directory of Open Access Journals (Sweden)

    Roberto Pereira-Arroyo

    2014-02-01

    Full Text Available In this paper, the problem at hand consists in the sizing of an Operational Transconductance Amplifier (OTA. The Pareto front is introduced as a useful analysis concept in order to explore the design space of such analog circuit. A genetic algorithm (GA is employed to automatically detect this front in a process that efficiently finds optimal parameteriza­tions and their corresponding values in an aggregate fitness space. Since the problem is treated as a multi-objective optimization task, different measures of the amplifier like the transconductance, the slew rate, the linear range and the input capacitance are used as fitness functions. Finally, simulation results are pre­sented, using a standard 0,5μm CMOS technology.

  11. Fuzzy Multiobjective Reliability Optimization Problem of Industrial Systems Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Harish Garg

    2013-01-01

    systems by utilizing uncertain, limited, and imprecise data. In many practical situations where reliability enhancement is involved, the decision making is complicated because of the presence of several mutually conflicting objectives. Moreover, data collected or available for the systems are vague, ambiguous, qualitative, and imprecise in nature due to various practical constraints and hence create some difficulties in optimizing the design problems. To handle these problems, this work presents an interactive method for solving the fuzzy multiobjective optimization decision-making problem, which can be used for the optimization decision making of the reliability with two or more objectives. Based on the preference of the decision makers toward the objectives, fuzzy multi-objective optimization problem is converted into crisp optimization problem and then solved with evolutionary algorithm. The proposed approach has been applied to the decomposition unit of a urea fertilizer plant situated in the northern part of India producing 1500–2000 metric tons per day.

  12. Hierarchical Optimization of Material and Structure

    DEFF Research Database (Denmark)

    Rodrigues, Helder C.; Guedes, Jose M.; Bendsøe, Martin P.

    2002-01-01

    This paper describes a hierarchical computational procedure for optimizing material distribution as well as the local material properties of mechanical elements. The local properties are designed using a topology design approach, leading to single scale microstructures, which may be restricted...... in various ways, based on design and manufacturing criteria. Implementation issues are also discussed and computational results illustrate the nature of the procedure....

  13. Multi-objective optimal dispatch of distributed energy resources

    Science.gov (United States)

    Longe, Ayomide

    This thesis is composed of two papers which investigate the optimal dispatch for distributed energy resources. In the first paper, an economic dispatch problem for a community microgrid is studied. In this microgrid, each agent pursues an economic dispatch for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, a simple market structure is introduced as a framework for energy trades in a small community microgrid such as the Solar Village. It was found that both sellers and buyers benefited by participating in this market. In the second paper, Semidefinite Programming (SDP) for convex relaxation of power flow equations is used for optimal active and reactive dispatch for Distributed Energy Resources (DER). Various objective functions including voltage regulation, reduced transmission line power losses, and minimized reactive power charges for a microgrid are introduced. Combinations of these goals are attained by solving a multiobjective optimization for the proposed ORPD problem. Also, both centralized and distributed versions of this optimal dispatch are investigated. It was found that SDP made the optimal dispatch faster and distributed solution allowed for scalability.

  14. The Multiobjective Trajectory Optimization for Hypersonic Glide Vehicle Based on Normal Boundary Intersection Method

    Directory of Open Access Journals (Sweden)

    Zhengnan Li

    2016-01-01

    Full Text Available To solve the multiobjective optimization problem on hypersonic glider vehicle trajectory design subjected to complex constraints, this paper proposes a multiobjective trajectory optimization method that combines the boundary intersection method and pseudospectral method. The multiobjective trajectory optimization problem (MTOP is established based on the analysis of the feature of hypersonic glider vehicle trajectory. The MTOP is translated into a set of general optimization subproblems by using the boundary intersection method and pseudospectral method. The subproblems are solved by nonlinear programming algorithm. In this method, the solution that has been solved is employed as the initial guess for the next subproblem so that the time consumption of the entire multiobjective trajectory optimization problem shortens. The maximal range and minimal peak heat problem is solved by the proposed method. The numerical results demonstrate that the proposed method can obtain the Pareto front of the optimal trajectory, which can provide the reference for the trajectory design of hypersonic glider vehicle.

  15. Interleaving Guidance in Evolutionary Multi-Objective Optimization

    Institute of Scientific and Technical Information of China (English)

    Lam Thu Bui; Kalyanmoy Deb; Hussein A. Abbass; Daryl Essam

    2008-01-01

    In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres (local models) in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Pareto front by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Pareto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version.

  16. A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems

    Institute of Scientific and Technical Information of China (English)

    林丹; 赵瑞

    2004-01-01

    This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper. This approach offers promise for the portfolio problems in practice.

  17. A multiobjective shape optimization study for a subsonic submerged inlet

    Science.gov (United States)

    Taskinoglu, Ezgi S.

    The purpose of the present work is to summarize the findings of a multiobjective shape optimization study conducted for a subsonic submerged air vehicle inlet. The objective functions of the optimization problem are distortion and swirl indices defined by the distribution of flow parameters over the exit cross-section of the inlet. The geometry alteration is performed by placing a protrusion in the shape of a fin on the baseline inlet surface. Thus, the design variables of the optimization problem are chosen to be the geometrical parameters defining the fin protrusion; namely fin height, length and incidence angle. The Trade Off (also known as epsilon-constraint) method is employed for finding the Pareto optimal set formed by the nondominated solutions of the feasible design space. Since the flow domain solution is required for every step along the line search, an automated optimization loop is constructed by integrating the optimizer with a surface modeler, a mesh generator and a flow solver through which the flow parameters over the compressor face are computed. In addition, the trade study for fin protrusion, the analyses and the comparison of the baseline and Pareto optimal solutions are presented and observations concerning grid resolution and convergence behaviour are discussed. The results display an irregular and discontinuous Pareto optimal set. Optimum inlet designs are scattered in two regions from which one representative inlet design is chosen and analyzed. As a result, it is concluded that an inlet designer has two options within the framework of this optimization study: an inlet design with high swirl but low distortion or an inlet design with low swirl but higher distortion.

  18. Multiobjective optimization in a pseudometric objective space as applied to a general model of business activities

    Science.gov (United States)

    Khachaturov, R. V.

    2016-09-01

    It is shown that finding the equivalence set for solving multiobjective discrete optimization problems is advantageous over finding the set of Pareto optimal decisions. An example of a set of key parameters characterizing the economic efficiency of a commercial firm is proposed, and a mathematical model of its activities is constructed. In contrast to the classical problem of finding the maximum profit for any business, this study deals with a multiobjective optimization problem. A method for solving inverse multiobjective problems in a multidimensional pseudometric space is proposed for finding the best project of firm's activities. The solution of a particular problem of this type is presented.

  19. An Optimal SVM with Feature Selection Using Multiobjective PSO

    Directory of Open Access Journals (Sweden)

    Iman Behravan

    2016-01-01

    Full Text Available Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM depends on different parameters such as penalty factor, C, and the kernel factor, σ. Also choosing an appropriate kernel function can improve the recognition score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computational time and complexity. So this is an optimization problem which can be solved by heuristic algorithm. In some cases besides the recognition score, the reliability of the classifier’s output is important. So in such cases a multiobjective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function, and select the best feature subset simultaneously in order to optimize the recognition score and the reliability of the SVM concurrently. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM. The results of the proposed method are compared to those which are achieved by single SVM, RBF, and MLP neural networks.

  20. 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.

  1. Determination of Pareto frontier in multi-objective maintenance optimization

    Energy Technology Data Exchange (ETDEWEB)

    Certa, Antonella [Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universita di Palermo 90128 Palermo (Italy); Galante, Giacomo, E-mail: galante@dtpm.unipa.i [Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universita di Palermo 90128 Palermo (Italy); Lupo, Toni; Passannanti, Gianfranco [Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universita di Palermo 90128 Palermo (Italy)

    2011-07-15

    The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series-parallel system.

  2. Tour Route Multiobjective Optimization Design Based on the Tourist Satisfaction

    Directory of Open Access Journals (Sweden)

    Yan Han

    2014-01-01

    Full Text Available The question prompted is how to design the tour route to make the tourists get the maximum satisfactions considering the tourists’ demand. The influence factors of the tour route choices of tourists were analyzed and tourists’ behavior characteristics and psychological preferences were regarded as the important influence factors based on the tourist behavioral theories. A questionnaire of tourists’ tour route information and satisfaction degree was carried out. Some information about the scene spot and tourists demand and tour behaviors characteristic such as visit frequency, number of attractions visited was obtained and analyzed. Based on the convey datum, tour routes multiobjective optimization functions were prompted for the tour route design regarding the maximum satisfaction and the minimum tour distance as the optimal objective. The available routes are listed and categorized. Based on the particle swarm optimization model, the priorities of the tour route are calculated and finally the suggestion depth tour route and quick route tour routes are given considering the different tour demands of tourists. The results can offer constructive suggestions on how to design tour routes on the part of tourism enterprises and how to choose a proper tour route on the part of tourists.

  3. Multi-objective Optimization of Process Performances when Cutting Carbon Steel with Abrasive Water Jet

    Directory of Open Access Journals (Sweden)

    M. Radovanović

    2016-12-01

    Full Text Available Multi-objective optimization of process performances (perpendicularity deviation, surface roughness and productivity when cutting carbon steel EN S235 with abrasive water jet is presented in this paper. Cutting factors (abrasive flow rate, traverse rate and standoff distance were determined when perpendicularity deviation and surface roughness are minimal and productivity is maximal. Multi-objective genetic algorithm (MOGA was used for the determination set of nondominated optimal points, known as Pareto front.

  4. A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking

    Institute of Scientific and Technical Information of China (English)

    Shi Chuan; Kang Li-shan; Li Yan; Yan Zhen-yu

    2003-01-01

    Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare to front, retain the diversity of the population, and use less time.

  5. Simulation and experimental validation of powertrain mounting bracket design obtained from multi-objective topology optimization

    OpenAIRE

    Qinghai Zhao; Xiaokai Chen; Lu Wang; Jianfeng Zhu; Zheng-Dong Ma; Yi Lin

    2015-01-01

    A framework of multi-objective topology optimization for vehicle powertrain mounting bracket design with consideration of multiple static and dynamic loading conditions is developed in this article. Incorporating into the simplified isotropic material with penalization model, compromise programming method is employed to describe the multi-objective and multi-stiffness topology optimization under static loading conditions, whereas mean eigenvalue formulation is proposed to analyze vibration op...

  6. Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

    Directory of Open Access Journals (Sweden)

    K. Vijayakumar

    2012-01-01

    Full Text Available Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1 transmission line over load and (2 congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

  7. A multiobjective optimization framework for multicontaminant industrial water network design.

    Science.gov (United States)

    Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge

    2011-07-01

    The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Multi-Objective Optimization of A PCHE Channels

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Moon; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of)

    2011-10-15

    High-temperature, gas-cooled nuclear reactors with a closed gas turbine cycle are recently being considered as a nuclear power generation concept for the future. In theory, the gas turbine cycle has an advantage in terms of simplicity and efficiency compared to the steam turbine cycle. However, since gas is used as the working fluid, inefficiency due to large volumes is inevitable, and a heat exchanger is used as a recuperator and pre-cooler. To solve this problem, different types of heat exchanger are needed. One of the alternative heat exchangers is the printed circuit heat exchanger (PCHE) developed by HEATRIC. Each flow channel of the PCHE is made through chemical etching on the surface of metal plates, and the typical PCHE channels on each plate have a zigzag shape to promote the heat transfer between the cold and hot channels. In this work, the zigzag flow channels of the PCHE have been optimized by using three-dimensional RANS analysis and a hybrid multi-objective evolutionary algorithm coupled with the RSA model. The cold channel angle and the ellipse aspect ratio of the cold channel are employed as the design variables. A group of optimal shapes are presented through Paretooptimal front (POF) by an {epsilon}-constraint strategy through an NSGA-II algorithm

  9. Multi-objective reliability-based optimization with stochastic metamodels.

    Science.gov (United States)

    Coelho, Rajan Filomeno; Bouillard, Philippe

    2011-01-01

    This paper addresses continuous optimization problems with multiple objectives and parameter uncertainty defined by probability distributions. First, a reliability-based formulation is proposed, defining the nondeterministic Pareto set as the minimal solutions such that user-defined probabilities of nondominance and constraint satisfaction are guaranteed. The formulation can be incorporated with minor modifications in a multiobjective evolutionary algorithm (here: the nondominated sorting genetic algorithm-II). Then, in the perspective of applying the method to large-scale structural engineering problems--for which the computational effort devoted to the optimization algorithm itself is negligible in comparison with the simulation--the second part of the study is concerned with the need to reduce the number of function evaluations while avoiding modification of the simulation code. Therefore, nonintrusive stochastic metamodels are developed in two steps. First, for a given sampling of the deterministic variables, a preliminary decomposition of the random responses (objectives and constraints) is performed through polynomial chaos expansion (PCE), allowing a representation of the responses by a limited set of coefficients. Then, a metamodel is carried out by kriging interpolation of the PCE coefficients with respect to the deterministic variables. The method has been tested successfully on seven analytical test cases and on the 10-bar truss benchmark, demonstrating the potential of the proposed approach to provide reliability-based Pareto solutions at a reasonable computational cost.

  10. Multi-objective Fuzzy Optimization Algorithm for Separation-Recycle System

    Institute of Scientific and Technical Information of China (English)

    孙力; 樊希山; 姚平经

    2004-01-01

    Separation-recycle system is an important part in chemical process, and its optimization is a multiobjective problem. In this paper the process optimization procedure is proposed. The fuzzy optimization algorithm with the concept of relative importance degree (RID) is utilized to transfer multi-objective optimization (MO-O) model into a single-objective optimization (SO-O) framework. The treatment of process condensate in synthesisa mmonia plant is taken as example to illustrate the optimization procedure, and the satisfactory result demonstrates feasibility and effectiveness of the suggested method.

  11. Development of Multiobjective Optimization Techniques for Sonic Boom Minimization

    Science.gov (United States)

    Chattopadhyay, Aditi; Rajadas, John Narayan; Pagaldipti, Naryanan S.

    1996-01-01

    A discrete, semi-analytical sensitivity analysis procedure has been developed for calculating aerodynamic design sensitivities. The sensitivities of the flow variables and the grid coordinates are numerically calculated using direct differentiation of the respective discretized governing equations. The sensitivity analysis techniques are adapted within a parabolized Navier Stokes equations solver. Aerodynamic design sensitivities for high speed wing-body configurations are calculated using the semi-analytical sensitivity analysis procedures. Representative results obtained compare well with those obtained using the finite difference approach and establish the computational efficiency and accuracy of the semi-analytical procedures. Multidisciplinary design optimization procedures have been developed for aerospace applications namely, gas turbine blades and high speed wing-body configurations. In complex applications, the coupled optimization problems are decomposed into sublevels using multilevel decomposition techniques. In cases with multiple objective functions, formal multiobjective formulation such as the Kreisselmeier-Steinhauser function approach and the modified global criteria approach have been used. Nonlinear programming techniques for continuous design variables and a hybrid optimization technique, based on a simulated annealing algorithm, for discrete design variables have been used for solving the optimization problems. The optimization procedure for gas turbine blades improves the aerodynamic and heat transfer characteristics of the blades. The two-dimensional, blade-to-blade aerodynamic analysis is performed using a panel code. The blade heat transfer analysis is performed using an in-house developed finite element procedure. The optimization procedure yields blade shapes with significantly improved velocity and temperature distributions. The multidisciplinary design optimization procedures for high speed wing-body configurations simultaneously

  12. 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.

  13. Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning

    Science.gov (United States)

    Englander, Jacob

    2015-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. Because low-thrust trajectory design is tightly coupled with systems design, power and propulsion characteristics must be chosen as well. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The methods is demonstrated on hypothetical mission to the main asteroid belt and to Deimos.

  14. A procedure for multi-objective optimization of tire design parameters

    Directory of Open Access Journals (Sweden)

    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.

  15. 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.

  16. Sensitivity analysis of multi-objective optimization of CPG parameters for quadruped robot locomotion

    Science.gov (United States)

    Oliveira, Miguel; Santos, Cristina P.; Costa, Lino

    2012-09-01

    In this paper, a study based on sensitivity analysis is performed for a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm based on NSGA-II. In this system, CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, a multi-objective problem with three conflicting objectives is formulated: maximization of the velocity, the wide stability margin and the behavioral diversity. The experimental results highlight the effectiveness of this multi-objective approach and the importance of the objectives to find different walking gait solutions for the quadruped robot.

  17. Economic and environmental multi-objective optimization to evaluate the impact of Belgian policy on solar power and electric vehicles

    OpenAIRE

    De Schepper, Ellen; Van Passel, Steven; Lizin, Sebastien; Vincent, Thomas; Martin, Benjamin; Gandibleux, Xavier

    2015-01-01

    This research uses multi-objective optimization to determine the optimal mixture of energy and transportation technologies, while optimizing economic and environmental impacts. We demonstrate the added value of using multi-objective mixed integer linear programming (MOMILP) considering economies of scale versus using continuous multi-objective linear programming (MOLP) assuming average cost intervals. This paper uses an improved version to solve MOMILPs exactly (Vincent, et al. 2013). To diff...

  18. Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

    Directory of Open Access Journals (Sweden)

    MadhuSudana Rao Nalluri

    2017-01-01

    Full Text Available With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM and multilayer perceptron (MLP technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs. Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

  19. Sufficient Optimality Conditions for Multiobjective Programming Involving (V, ρ) h,ψ-type Ⅰ Functions

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qing-xiang; JIANG Yan; KANG Rui-rui

    2012-01-01

    New classes of functions namely (V,ρ)h,ψ-type Ⅰ,quasi (V,ρ)h,ψ-type Ⅰ and pseudo (V,ρ)h,ψ-type Ⅰ functions are defined for multiobjective programming problem by using BenTal's generalized algebraic operation.The examples of (V,ρ)h.ψ-type Ⅰ functions are given.The sufficient optimality conditions are obtained for multi-objective programming problem involving above new generalized convexity.

  20. Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems

    DEFF Research Database (Denmark)

    Vlachogiannis, Ioannis (John); Lee, K Y

    2009-01-01

    In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem...... of steady-state of power systems. Specifically, reactive power control is formulated as a multi-objective optimization problem and solved using the parallel VEPSO algorithm. The results on the IEEE 30-bus test system are compared with those given by another multi-objective evolutionary technique...... demonstrating the advantage of parallel VEPSO. The parallel VEPSO is also tested on a larger power system this with 136 busses. (C) 2009 Elsevier Ltd. All rights reserved....

  1. Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm

    Science.gov (United States)

    Wen, Feng; Gen, Mitsuo; Yu, Xinjie

    This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

  2. Joint Geophysical Inversion With Multi-Objective Global Optimization Methods

    Science.gov (United States)

    Lelievre, P. G.; Bijani, R.; Farquharson, C. G.

    2015-12-01

    Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.

  3. Geophysical Inversion With Multi-Objective Global Optimization Methods

    Science.gov (United States)

    Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin

    2016-04-01

    We are investigating the use of Pareto multi-objective global optimization (PMOGO) methods to solve numerically complicated geophysical inverse problems. PMOGO methods can be applied to highly nonlinear inverse problems, to those where derivatives are discontinuous or simply not obtainable, and to those were multiple minima exist in the problem space. PMOGO methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. This allows a more complete assessment of the possibilities and provides opportunities to calculate statistics regarding the likelihood of particular model features. We are applying PMOGO methods to four classes of inverse problems. The first are discrete-body problems where the inversion determines values of several parameters that define the location, orientation, size and physical properties of an anomalous body represented by a simple shape, for example a sphere, ellipsoid, cylinder or cuboid. A PMOGO approach can determine not only the optimal shape parameters for the anomalous body but also the optimal shape itself. Furthermore, when one expects several anomalous bodies in the subsurface, a PMOGO inversion approach can determine an optimal number of parameterized bodies. The second class of inverse problems are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The third class of problems are lithological inversions, which are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the fourth class, surface geometry inversions, we consider a fundamentally different type of problem in which a model comprises wireframe surfaces representing contacts between rock units. The physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. Surface geometry inversion can be

  4. A multi-objective approach in the optimization of optical systems taking into account tolerancing

    Science.gov (United States)

    de Albuquerque, Bráulio F. C.; Liao, Lin-Yao; Montes, Amauri Silva; de Sousa, Fabiano Luis; Sasián, José

    2011-10-01

    A Multi-Objective approach for lens design optimization was verified. The optimization problem was approached by addressing simultaneously, but separately, image quality and system tolerancing. In contrast to other previous published methods, the error functions were not combined into a single merit function. As a result the method returns a set of nondominated solutions that generates a Pareto Front. Our method resulted in alternate and useful insights about the trade off solutions for a lens design problem. This Multi-objective optimization can conveniently be implemented with evolutionary methods of optimization that have established success in lens design. We provided an example of the insights and usefulness of our approach in the design of a Telephoto lens system using NSGA-II, a popular multiobjective evolutionary optimization algorithm.

  5. A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor networks.

    Science.gov (United States)

    Masazade, Engin; Rajagopalan, Ramesh; Varshney, Pramod K; Mohan, Chilukuri K; Sendur, Gullu Kiziltas; Keskinoz, Mehmet

    2010-04-01

    For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

  6. Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method.

    Science.gov (United States)

    Zhang, Rui; Xie, Wen-Ming; Yu, Han-Qing; Li, Wen-Wei

    2014-04-01

    An improved multi-objective optimization (MOO) model was established and used for simultaneously optimizing the treatment cost and multiple effluent quality indexes (including effluent COD, NH4(+)-N, NO3(-)-N) of a municipal wastewater treatment plant (WWTP). Compared with previous models that were mainly based on the use of fixed decision factors and did not taken into account the treatment cost, this model introduces a relationship model based on back propagation algorithm to determine the set of decision factors according to the expected optimization targets. Thus, a more flexible and precise optimization of the treatment process was allowed. Moreover, a MOO of conflicting objectives (i.e., treatment cost and effluent quality) was achieved. Applying this method, an optimal balance between operating cost and effluent quality of a WWTP can be found. This model may offer a useful tool for optimized design and control of practical WWTPs.

  7. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-06-15

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

  9. 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 ...

  10. Multi-Objective Optimization of Mechanical Running Conditions of Large Scale Statically Indeterminate Rotary Kiln

    Institute of Scientific and Technical Information of China (English)

    Hu Xiaoping; Xiao Yougang; Wang Guangbin

    2006-01-01

    Combined with the second rotary kiln of Alumina Factory in Great Wall Aluminum Company, the mechanics characteristics of statically indeterminate large-scale rotary kiln with variable cross-sections is analyzed. In order to adjusting the runing axis of rotary kiln, taking the force equilibrium of the rollers and the minimum of relative axis deflection as the optimization goal, the multi-objective optimization model of mechanical running conditions of kiln rotary is set up. The mechanical running conditions of the second rotary kiln after multi-objective optimization adjustment are compared with those before adjustment and after routine adjustment. It shows that multi-objective optimization adjustment can make axis as direct as possible and can distribute kiln loads equally.

  11. Synthesis of Phase-Only Reconfigurable Linear Arrays Using Multiobjective Invasive Weed Optimization Based on Decomposition

    Directory of Open Access Journals (Sweden)

    Yan Liu

    2014-01-01

    Full Text Available Synthesis of phase-only reconfigurable array aims at finding a common amplitude distribution and different phase distributions for the array to form different patterns. In this paper, the synthesis problem is formulated as a multiobjective optimization problem and solved by a new proposed algorithm MOEA/D-IWO. First, novel strategies are introduced in invasive weed optimization (IWO to make original IWO fit for solving multiobjective optimization problems; then, the modified IWO is integrated into the framework of the recently well proved competitive multiobjective optimization algorithm MOEA/D to form a new competitive MOEA/D-IWO algorithm. At last, two sets of experiments are carried out to illustrate the effectiveness of MOEA/D-IWO. In addition, MOEA/D-IWO is compared with MOEA/D-DE, a new version of MOEA/D. The comparing results show the superiority of MOEA/D-IWO and indicate its potential for solving the antenna array synthesis problems.

  12. Multi-Objective Optimization of Water-Sedimentation-Power in Reservoir Based on Pareto-Optimal Solution

    Institute of Scientific and Technical Information of China (English)

    LI Hui; LIAN Jijian

    2008-01-01

    A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover,the inertia weight serf-adjusting mechanism and Pareto-optimal archive are introduced into the par-ticle swarm optimization and an improved multi-objective particle swarm optimization (IMOPSO) is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions (A, B, C) of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sedi-ment deposition, sediment deposition and power generation, and power generation, respectively.The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Com-pared with the non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ), IMOPSO has close global op-timization capability and is suitable for multi-objective optimization problems.

  13. Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

    Science.gov (United States)

    Yu, Xiang; Zhang, Xueqing

    2017-01-01

    Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

  14. Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

    Science.gov (United States)

    Yu, Xiang; Zhang, Xueqing

    2017-01-01

    Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run. PMID:28192508

  15. Optimal design of multichannel fiber Bragg grating filters using Pareto multi-objective optimization algorithm

    Science.gov (United States)

    Chen, Jing; Liu, Tundong; Jiang, Hao

    2016-01-01

    A Pareto-based multi-objective optimization approach is proposed to design multichannel FBG filters. Instead of defining a single optimal objective, the proposed method establishes the multi-objective model by taking two design objectives into account, which are minimizing the maximum index modulation and minimizing the mean dispersion error. To address this optimization problem, we develop a two-stage evolutionary computation approach integrating an elitist non-dominated sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS). NSGA-II is utilized to search for the candidate solutions in terms of both objectives. The obtained results are provided as Pareto front. Subsequently, the best compromise solution is determined by the TOPSIS method from the Pareto front according to the decision maker's preference. The design results show that the proposed approach yields a remarkable reduction of the maximum index modulation and the performance of dispersion spectra of the designed filter can be optimized simultaneously.

  16. An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

    Directory of Open Access Journals (Sweden)

    Xiangmin Guan

    2015-01-01

    Full Text Available Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

  17. Multiobjective Optimization for Electronic Circuit Design in Time and Frequency Domains

    Directory of Open Access Journals (Sweden)

    J. Dobes

    2013-04-01

    Full Text Available The multiobjective optimization provides an extraordinary opportunity for the finest design of electronic circuits because it allows to mathematically balance contradictory requirements together with possible constraints. In this paper, an original and substantial improvement of an existing method for the multiobjective optimization known as GAM (Goal Attainment Method is suggested. In our proposal, the GAM algorithm itself is combined with a procedure that automatically provides a set of parameters -- weights, coordinates of the reference point -- for which the method generates noninferior solutions uniformly spread over an appropriately selected part of the Pareto front. Moreover, the resulting set of obtained solutions is then presented in a suitable graphic form so that the solution representing the most satisfactory tradeoff can be easily chosen by the designer. Our system generates various types of plots that conveniently characterize results of up to four-dimensional problems. Technically, the procedures of the multiobjective optimization were created as a software add-on to the CIA (Circuit Interactive Analyzer program. This way enabled us to utilize many powerful features of this program, including the sensitivity analyses in time and frequency domains. As a result, the system is also able to perform the multiobjective optimization in the time domain and even highly nonlinear circuits can be significantly improved by our program. As a demonstration of this feature, a multiobjective optimization of a C-class power amplifier in the time domain is thoroughly described in the paper. Further, a four-dimensional optimization of a video amplifier is demonstrated with an original graphic representation of the Pareto front, and also some comparison with the weighting method is done. As an example of improving noise properties, a multiobjective optimization of a low-noise amplifier is performed, and the results in the frequency domain are shown

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  19. Reduction method with system analysis for multiobjective optimization-based design

    Science.gov (United States)

    Azarm, S.; Sobieszczanski-Sobieski, J.

    1993-01-01

    An approach for reducing the number of variables and constraints, which is combined with System Analysis Equations (SAE), for multiobjective optimization-based design is presented. In order to develop a simplified analysis model, the SAE is computed outside an optimization loop and then approximated for use by an operator. Two examples are presented to demonstrate the approach.

  20. Comparison of Direct Multiobjective Optimization Methods for the Design of Electric Vehicles

    OpenAIRE

    2006-01-01

    International audience; "System design oriented methodologies" are discussed in this paper through the comparison of multiobjective optimization methods applied to heterogeneous devices in electrical engineering. Avoiding criteria function derivatives, direct optimization algorithms are used. In particular, deterministic geometric methods such as the Hooke & Jeeves heuristic approach are compared with stochastic evolutionary algorithms (Pareto genetic algorithms). Different issues relative to...

  1. Ensemble-based multi-objective optimization of on-off control devices under geological uncertainty

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Rossa, E.D.; Hof, P.M.J. van den; Jansen, J.D.

    2015-01-01

    We consider robust ensemble-based (EnOpt) multi-objective production optimization of on-off inflow control devices (ICDs) for a sector model inspired on a real-field case. The use of on-off valves as optimization variables leads to a discrete control problem. We propose a re-parameterization of such

  2. A GA-based Multi-Objective Optimization for Service Restoration in Power Distribution Systems

    Science.gov (United States)

    Inagaki, Jun; Nakajima, Jun; Haseyama, Miki; Kitajima, Hideo

    Service restoration problem in distribution systems is formulated as a multi-objective optimization problem which is demanded not only for minimizing the amount of unrestored total loads but also for minimizing the number of the switching operations. The solution of the multi-objective optimization problem is usually obtained with a set of Pareto optimal solutions. The Pareto optimal solutions for the service restoration problem are useful for users to obtain their desired restoration by comparing a Pareto optimal solution with the others. However, the conventional methods cannot obtain plural Pareto optimal solutions in one trial. Therefore, this paper proposes a method for obtaining a Pareto optimal set for the service restoration problem with a genetic algorithm. The genetic algorithm produces many possible solutions in its search process. By utilizing this feature, the proposed method can obtain the Pareto optimal set.

  3. Optimization of Hierarchical System for Data Acquisition

    Directory of Open Access Journals (Sweden)

    V. Novotny

    2011-04-01

    Full Text Available Television broadcasting over IP networks (IPTV is one of a number of network applications that are except of media distribution also interested in data acquisition from group of information resources of variable size. IP-TV uses Real-time Transport Protocol (RTP protocol for media streaming and RTP Control Protocol (RTCP protocol for session quality feedback. Other applications, for example sensor networks, have data acquisition as the main task. Current solutions have mostly problem with scalability - how to collect and process information from large amount of end nodes quickly and effectively? The article deals with optimization of hierarchical system of data acquisition. Problem is mathematically described, delay minima are searched and results are proved by simulations.

  4. Recognition of Gene Acceptor Site Based on Multi-objective Optimization

    Institute of Scientific and Technical Information of China (English)

    Jing ZHAO; Yue-Min ZHU; Pei-Ming SONG; Qing FANG; Jian-Hua LUO

    2005-01-01

    A new method for predicting the gene acceptor site based on multi-objective optimization is introduced in this paper. The models for the acceptor, branch and distance between acceptor site and branch site were constructed according to the characteristics of the sequences from the exon-intron database and using common biological knowledge. The acceptor function, branch function and distance function were defined respectively, and the multi-objective optimization model was constructed to recognize the splice site. The test results show that the algorithm used in this study performs better than the SplicePredictor,which is one of the leading acceptor site detectors.

  5. Multiobjective Optimization of Aircraft Maintenance in Thailand Using Goal Programming: A Decision-Support Model

    Directory of Open Access Journals (Sweden)

    Yuttapong Pleumpirom

    2012-01-01

    Full Text Available The purpose of this paper is to develop the multiobjective optimization model in order to evaluate suppliers for aircraft maintenance tasks, using goal programming. The authors have developed a two-step process. The model will firstly be used as a decision-support tool for managing demand, by using aircraft and flight schedules to evaluate and generate aircraft-maintenance requirements, including spare-part lists. Secondly, they develop a multiobjective optimization model by minimizing cost, minimizing lead time, and maximizing the quality under various constraints in the model. Finally, the model is implemented in the actual airline's case.

  6. Design Optimization of an Axial Fan Blade Through Multi-Objective Evolutionary Algorithm

    Science.gov (United States)

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

    2010-06-01

    This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ɛ -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.

  7. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Magnier, Laurent; Haghighat, Fariborz [Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. W., BE-351, Montreal, Quebec H3G 1M8 (Canada)

    2010-03-15

    Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption. (author)

  8. Supply Chain Network Optimization Based on Fuzzy Multiobjective Centralized Decision-Making Model

    Directory of Open Access Journals (Sweden)

    Xinyi Fu

    2017-01-01

    Full Text Available Supply chain cooperation strategy believes that the integration of the operation process can produce value for customers, optimize the supply chain of the connection between the vertical nodes, and constantly strengthen the performance of the advantages, so as to achieve mutual benefit and win-win results. Under fuzzy uncertain environment and centralized decision-making mode, we study multiobjective decision-making optimization, which focuses on equilibrium and compensation of multiobjective problems; that is to say, the proper adjustment of the individual goal satisfaction level will make other goals’ satisfaction levels change greatly. Through coordination among the multiobjectives, supply chain system will achieve global optimum. Finally, a simulation experiment from Shaoxing textile case is used to verify its efficiency and effectiveness.

  9. Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply chain environment.

    Science.gov (United States)

    Wang, Lin; Qu, Hui; Liu, Shan; Dun, Cai-xia

    2013-01-01

    As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted.

  10. Modeling and Optimization of the Multiobjective Stochastic Joint Replenishment and Delivery Problem under Supply Chain Environment

    Directory of Open Access Journals (Sweden)

    Lin Wang

    2013-01-01

    Full Text Available As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD decision. In this paper, a new multiobjective stochastic JRD (MSJRD of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE, hybrid DE (HDE, and genetic algorithm (GA, are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted.

  11. Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

    Science.gov (United States)

    Chu, J.; Zhang, C.; Fu, G.; Li, Y.; Zhou, H.

    2015-08-01

    This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

  12. Fuzzy logic multiobjective optimization for stand-alone photovoltaic plants

    Energy Technology Data Exchange (ETDEWEB)

    Tina, G.; Adorno, G.; Ragusa, C.

    1998-07-01

    The power generation by renewable energy sources involves a wide series of technical and economic problems, which condition its applications on a vast scale. Photovoltaic power generation presents many problems hardly to solve, such as: high cost in generating power and the power quality. In this background it is to insert the topic of optimal dimensioning of the PV power plants, meaning the achievement of an acceptable compromise between the power quality and the plant costs. This problem affects above all the small and medium size plants, such as: stand-alone PV domestic supply, where an incorrect dimensioning may cause difficulties to the functionality of the plant or higher costs. In this paper a method of optimal dimensioning of a PV power plant with battery storage is shown, but it is suitable to optimise also hybrid plants, which are based on the simultaneous presence of other and different energy sources. The project variables are the area and tilt of the PV modules and the accumulator capacity, whereas the project quality indexes (the objects to optimise) are the total plant cost and the supplied ratio of the electrical load. The calculation of the total ratio of load supplied was tackled using the availability method, which seems to be the one which best allows to make a long-term forecast, because of the systematic experimental studies over the last forty years and because of it correlates the solar radiation variability in the course of the day and of the year with the producible power. The economic cost has been calculated considering the plant's technical life, referring to current money all the future costs (by LCC, Life-Cycle Costing, method) and annualising them (by ALCC, Annualise Life-Cycle Costing, method) in order to obtain a total cost per kWh to compare with other power source costs. Particularly, the PV plant's cost has been related to the ALCC cost in case of supplying the electrical load connected to national grid. Since the

  13. Multiobjective optimization design of a fractional order PID controller for a gun control system.

    Science.gov (United States)

    Gao, Qiang; Chen, Jilin; Wang, Li; Xu, Shiqing; Hou, Yuanlong

    2013-01-01

    Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method.

  14. Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model

    Institute of Scientific and Technical Information of China (English)

    WANG Ya-lin; MA Jie; GUI Wei-hua; YANG Chun-hua; ZHANG Chuan-fu

    2006-01-01

    A multi-objective intelligent coordinating optimization strategy based on qualitative and quantitative synthetic model for Pb-Zn sintering blending process was proposed to obtain optimal mixture ratio. The mechanism and neural network quantitative models for predicting compositions and rule models for expert reasoning were constructed based on statistical data and empirical knowledge. An expert reasoning method based on these models were proposed to solve blending optimization problem, including multi-objective optimization for the first blending process and area optimization for the second blending process, and to determine optimal mixture ratio which will meet the requirement of intelligent coordination. The results show that the qualified rates of agglomerate Pb, Zn and S compositions are increased by 7.1%, 6.5% and 6.9%, respectively, and the fluctuation of sintering permeability is reduced by 7.0 %, which effectively stabilizes the agglomerate compositions and the permeability.

  15. Multiobjective Optimization Design of Double-Row Blades Hydraulic Retarder with Surrogate Model

    Directory of Open Access Journals (Sweden)

    Liu Chunbao

    2015-02-01

    Full Text Available For the design of double-row blades hydraulic retarder involves too many parameters, the solution process of the optimal parameter combination is characterized by the large calculation load, the long calculation time, and the high cost. In this paper, we proposed a multiobjective optimization method to obtain the optimal balanced solution between the braking torque and volume of double-row blades hydraulic retarder. Moreover, we established the surrogate model for objective function with radial basis function (RBF, thus avoiding the time-consuming three-dimensional modeling and fluid simulation. Then, nondominated sorting genetic algorithm-II (NSGA-II was adopted to obtain the optimal combination solution of design variables. Moreover, the comparison results of computational fluid dynamics (CFD values of the optimal combination parameters and original design parameters indicated that the multiobjective optimization method based on surrogate model was applicable for the design of double-row blades hydraulic retarder.

  16. Multi-objective optimization in formation tasks of leather and fur materials

    Directory of Open Access Journals (Sweden)

    Ольга Викторовна Сангинова

    2014-09-01

    Full Text Available The comparative analysis of the efficiency of different ways to obtain a compromise solution in the multi-objective constrained optimization tasks has been conducted. The analysis was performed for a number of innovative technologies of leather and fur materials forming.

  17. Improving multi-objective reservoir operation optimization with sensitivity-informed problem decomposition

    Science.gov (United States)

    Chu, J. G.; Zhang, C.; Fu, G. T.; Li, Y.; Zhou, H. C.

    2015-04-01

    This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce the computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed problem decomposition dramatically reduces the computational demands required for attaining high quality approximations of optimal tradeoff relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed problem decomposition and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform problem decomposition when solving the complex multi-objective reservoir operation problems.

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

    CERN Document Server

    Tydrichova, Magdalena

    2017-01-01

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

  19. Approximating the Pareto Set of Multiobjective Linear Programs via Robust Optimization

    NARCIS (Netherlands)

    Gorissen, B.L.; den Hertog, D.

    2012-01-01

    Abstract: The Pareto set of a multiobjective optimization problem consists of the solutions for which one or more objectives can not be improved without deteriorating one or more other objectives. We consider problems with linear objectives and linear constraints and use Adjustable Robust Optimizati

  20. CHESS-changing horizon efficient set search: A simple principle for multiobjective optimization

    DEFF Research Database (Denmark)

    Borges, Pedro Manuel F. C.

    2000-01-01

    This paper presents a new concept for generating approximations to the non-dominated set in multiobjective optimization problems. The approximation set A is constructed by solving several single-objective minimization problems in which a particular function D(A, z) is minimized. A new algorithm...

  1. Multi-objective optimization of riparian buffer networks; valuing present and future benefits

    Science.gov (United States)

    Multi-objective optimization has emerged as a popular approach to support water resources planning and management. This approach provides decision-makers with a suite of management options which are generated based on metrics that represent different social, economic, and environ...

  2. 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...

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

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Hattel, Jesper Henri

    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...

  4. CHESS-changing horizon efficient set search: A simple principle for multiobjective optimization

    DEFF Research Database (Denmark)

    Borges, Pedro Manuel F. C.

    2000-01-01

    This paper presents a new concept for generating approximations to the non-dominated set in multiobjective optimization problems. The approximation set A is constructed by solving several single-objective minimization problems in which a particular function D(A, z) is minimized. A new algorithm t...

  5. Solving multiobjective optimal reactive power dispatch using modified NSGA-II

    Energy Technology Data Exchange (ETDEWEB)

    Jeyadevi, S.; Baskar, S.; Babulal, C.K.; Willjuice Iruthayarajan, M. [Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu 625 015 (India)

    2011-02-15

    This paper addresses an application of modified NSGA-II (MNSGA-II) by incorporating controlled elitism and dynamic crowding distance (DCD) strategies in NSGA-II to multiobjective optimal reactive power dispatch (ORPD) problem by minimizing real power loss and maximizing the system voltage stability. To validate the Pareto-front obtained using MNSGA-II, reference Pareto-front is generated using multiple runs of single objective optimization with weighted sum of objectives. For simulation purposes, IEEE 30 and IEEE 118 bus test systems are considered. The performance of MNSGA-II, NSGA-II and multiobjective particle swarm optimization (MOPSO) approaches are compared with respect to multiobjective performance measures. TOPSIS technique is applied on obtained non-dominated solutions to determine best compromise solution (BCS). Karush-Kuhn-Tucker (KKT) conditions are also applied on the obtained non-dominated solutions to substantiate a claim on optimality. Simulation results are quite promising and the MNSGA-II performs better than NSGA-II in maintaining diversity and authenticates its potential to solve multiobjective ORPD effectively. (author)

  6. Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

    Science.gov (United States)

    Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

    2010-06-01

    This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

  7. Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jun-hong; XIE An-guo; SHEN Feng-man

    2007-01-01

    A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.

  8. Multi-objective dynamic optimization model for China's road transport energy technology switching

    Institute of Scientific and Technical Information of China (English)

    Dan GAO; Zheng LI; Feng FU; Linwei MA

    2009-01-01

    Deducting the future switching of the road transport energy technology is one of the key preconditions for relative technology development planning. However,one of the difficulties is to address the issue of multi-objective and conflicting constrains, e.g., minimizing the climate mitigation or minimizing economic cost. In this paper, a dynamic optimization model was established, which can be used to analyze the road transport energy technology switching under multi-objective constrains.Through one case study, a series of solutions could be derived to provide decision-makers with the flexibility to choose the appropriate solution with respect to the given situation.

  9. Multi-Objective Optimization of A Semisubmersible for Ultra-Deep Water

    Institute of Scientific and Technical Information of China (English)

    CHEN Xin-quan; TAN Jia-hua

    2008-01-01

    Semisubmersible will work well when oil exploitation goes to ultra-deep water because of its variable load capacities, and good motion performance in extreme waves. It is considered to be a main type of platform while the water depth is more than 3000 meters. This paper establishes a multi-objective optimization model of semisubmersible for ultra-deep water, and it is solved by a multi-objective genetic algorithm-NSGA-II. The model is applied to a practical design, and Pareto results are obtained. The effectiveness of the method is verified by hydrodynamic analysis.

  10. Optimal Vegetation Maintenance Scheduling Underneath Aerial Power Distribution Systems Using an Optimization Multiobjective Technique

    Directory of Open Access Journals (Sweden)

    Arias-Londoño Andrés

    2014-01-01

    Full Text Available One of the major causes for the interruption of power service supply is the contact between vegetation and the power distribution lines. In this paper, two multiobjective mathematical models are proposed to minimize the vegetation negative impact on the electricity network quality, minimizing in turn, the cost of the vegetation pruning. In the first mathematical model, the level of energy not served due the failures from vegetation is minimized and in the second one the average percentage of violation into the safe zone between the vegetation and the overhead power distribution systems is minimized. In both models, the second objective function is to minimize the cost of maintenance of vegetation, considering restrictions associated with equipment availability, reliability in the electrical service and maximum number of prunings on a network segment for the period of vegetation maintenance planning. The scheduling result is pruning activities for a planning period of one year. The elitist non-dominated sorting genetic algorithm (NSGA-II is the multi-objective optimization technique used to solve this problem on a test system.

  11. Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

    Directory of Open Access Journals (Sweden)

    Yongpeng Shen

    2016-02-01

    Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

  12. Multi-objective optimization of empirical hydrological model for streamflow prediction

    Science.gov (United States)

    Guo, Jun; Zhou, Jianzhong; Lu, Jiazheng; Zou, Qiang; Zhang, Huajie; Bi, Sheng

    2014-04-01

    Traditional calibration of hydrological models is performed with a single objective function. Practical experience with the calibration of hydrologic models reveals that single objective functions are often inadequate to properly measure all of the characteristics of the hydrologic system. To circumvent this problem, in recent years, a lot of studies have looked into the automatic calibration of hydrological models with multi-objective functions. In this paper, the multi-objective evolution algorithm MODE-ACM is introduced to solve the multi-objective optimization of hydrologic models. Moreover, to improve the performance of the MODE-ACM, an Enhanced Pareto Multi-Objective Differential Evolution algorithm named EPMODE is proposed in this research. The efficacy of the MODE-ACM and EPMODE are compared with two state-of-the-art algorithms NSGA-II and SPEA2 on two case studies. Five test problems are used as the first case study to generate the true Pareto front. Then this approach is tested on a typical empirical hydrological model for monthly streamflow forecasting. The results of these case studies show that the EPMODE, as well as MODE-ACM, is effective in solving multi-objective problems and has great potential as an efficient and reliable algorithm for water resources applications.

  13. Simulation and experimental validation of powertrain mounting bracket design obtained from multi-objective topology optimization

    Directory of Open Access Journals (Sweden)

    Qinghai Zhao

    2015-06-01

    Full Text Available A framework of multi-objective topology optimization for vehicle powertrain mounting bracket design with consideration of multiple static and dynamic loading conditions is developed in this article. Incorporating into the simplified isotropic material with penalization model, compromise programming method is employed to describe the multi-objective and multi-stiffness topology optimization under static loading conditions, whereas mean eigenvalue formulation is proposed to analyze vibration optimization. To yield well-behaved optimal topologies, minimum member size and draw constraint are settled for meeting manufacturing feasibility requirements. The ultimate mounting bracket is reconstructed based on the optimum results. Numerical analyses of the bracket are performed, followed by physical tests. It is proven that topology optimization methodology is promising and effective for vehicle component design.

  14. 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.

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

    Science.gov (United States)

    Pang, X.; Rybarcyk, L. J.

    2014-03-01

    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.

  16. Multi-Objective Optimization Algorithms Design based on Support Vector Regression Metamodeling

    Directory of Open Access Journals (Sweden)

    Qi Zhang

    2013-11-01

    Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.

  17. Multi-Objective Optimization with Function Approximation Including Application to Computationally Expensive Groundwater Remediation Design

    Science.gov (United States)

    Akhtar, T.; Shoemaker, C. A.

    2009-12-01

    Water Resources design decisions frequently entail trade-offs between conflicting objectives, for instance cost minimization and contaminant(s) concentration minimization. Multi-objective optimization methods (including those based on evolutionary methods) typically require a very large number of simulations to find a solution. Many groundwater remediation problems are modeled by computationally intensive systems of Partial Differential Equations and simulations. Hence it is desirable that these models are calibrated via algorithms that require less number of simulations. A new strategy called Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS) is proposed for multi-objective optimization of computationally expensive problems. A multi-objective management framework is devised to analyze the trade-offs between conflicting objectives. We will present applications to test functions and to a groundwater contamination problem. The pumping rates at different well locations and management periods are the decision variables, and cost and contaminant concentration are the objectives to be minimized. The optimization strategy is iterative and makes use of Radial Basic Functions to develop response surfaces as an approximation of the computationally expensive objectives. A novel method called the Gap Optimization method is introduced. The gap optimization method incorporates use of a multi-objective evolutionary optimization (MOEA) method that is applied to select the next point for expensive evaluation and consequent improvement of the surrogate model. In order to provide sound alternatives to the decision makers, the evaluation point selection procedure strives to ensure that the final trade-off curve generated from the algorithm is close to the true Pareto front and includes a diverse set of solutions. After the final iteration, a set of candidate solutions is selected via the iterative Gap Optimization procedure and the last MOEA iteration, and

  18. Dynamic Cell Formation based on Multi-objective Optimization Model

    Directory of Open Access Journals (Sweden)

    Guozhu Jia

    2013-08-01

    Full Text Available In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.  

  19. Optimality Conditions for Nondifferentiable Multiobjective Semi-Infinite Programming Problems

    Directory of Open Access Journals (Sweden)

    D. Barilla

    2016-01-01

    Full Text Available We have considered a multiobjective semi-infinite programming problem with a feasible set defined by inequality constraints. First we studied a Fritz-John type necessary condition. Then, we introduced two constraint qualifications and derive the weak and strong Karush-Kuhn-Tucker (KKT in brief types necessary conditions for an efficient solution of the considered problem. Finally an extension of a Caristi-Ferrara-Stefanescu result for the (Φ,ρ-invexity is proved, and some sufficient conditions are presented under this weak assumption. All results are given in terms of Clark subdifferential.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Warid Warid

    Full Text Available 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.

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Patel G.C.M.

    2016-09-01

    Full Text Available The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.. It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA, particle swarm optimization (PSO and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.

  4. Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model

    Directory of Open Access Journals (Sweden)

    Yi Liu

    2016-01-01

    Full Text Available Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II on a daily streamflow forecasting model based on support vector machine (SVM. The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.

  5. Artificial Bee Colony Algorithm Based on K-Means Clustering for Multiobjective Optimal Power Flow Problem

    Directory of Open Access Journals (Sweden)

    Liling Sun

    2015-01-01

    Full Text Available An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II, the multiobjective particle swarm optimizer (MOPSO, and the multiobjective ABC (MOABC. Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF problem 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 demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

  6. Image Watermarking Algorithm Based on Multiobjective Ant Colony Optimization and Singular Value Decomposition in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Khaled Loukhaoukha

    2013-01-01

    Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.

  7. Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms

    Institute of Scientific and Technical Information of China (English)

    ANDRES-TOROB.; GIRON-SIERRAJ.M.; FERNANDEZ-BLANCOP.; LOPEZ-OROZCOJ.A.; BESADA-PORTASE.

    2004-01-01

    This paper describes empirical research on the model, optimization and supervisory control of beer fermentation.Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results.The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs).Successful finding of optimal ways to drive these processes were reported.Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.

  8. Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search

    Directory of Open Access Journals (Sweden)

    Zong Woo Geem

    2015-07-01

    Full Text Available Thus far, various phenomenon-mimicking algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping, ant colony optimization, harmony search, cross entropy, scatter search, and honey-bee mating, have been proposed to optimally design the water distribution networks with respect to design cost. However, flow velocity constraint, which is critical for structural robustness against water hammer or flow circulation against substance sedimentation, was seldom considered in the optimization formulation because of computational complexity. Thus, this study proposes a novel fuzzy-based velocity reliability index, which is to be maximized while the design cost is simultaneously minimized. The velocity reliability index is included in the existing cost optimization formulation and this extended multiobjective formulation is applied to two bench-mark problems. Results show that the model successfully found a Pareto set of multiobjective design solutions in terms of cost minimization and reliability maximization.

  9. Environmental Multiobjective Optimization of the Use of Biomass Resources for Energy

    DEFF Research Database (Denmark)

    Vadenbo, Carl; Tonini, Davide; Astrup, Thomas Fruergaard

    2017-01-01

    of the optimization model is exemplified by a case aimed at determining the environmentally optimal use of biomass in the Danish energy system in 2025. A multiobjective formulation based on fuzzy intervals for six environmental impact categories resulted in impact reductions of 13-43% compared to the baseline...... environmental consequences. To circumvent the limitations of scenario-based life cycle assessment (LCA), we develop a multiobjective optimization model to systematically identify the environmentally optimal use of biomass for energy under given system constraints. Besides satisfying annual final energy demand......, the model constraints comprise availability of biomass and arable land, technology- and system-specific capacities, and relevant policy targets. Efficiencies and environmental performances of bioenergy conversions are derived using biochemical process models combined with LCA data. The application...

  10. Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

    Science.gov (United States)

    Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

    2017-05-01

    To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

  11. MULTI-OBJECTIVE OPTIMIZATION OF EDM PARAMETERS USING GREY RELATION ANALYSIS

    Directory of Open Access Journals (Sweden)

    N. RADHIKA

    2015-01-01

    Full Text Available This paper involves the multi-objective optimization of process parameters of AlSi10Mg/9 wt% alumina/3 wt% graphite in Electrical Discharge Machining for obtaining minimum surface roughness, minimum tool wear rate and maximum material removal rate. The important machining parameters were selected as peak current, flushing pressure and pulse-on time. Experiments were conducted by selecting different operating levels for the three parameters according to Taguchi’s Design of Experiments. The multi-objective optimization was performed using Grey Relation Analysis to determine the optimal solution. The Grey Relation Grade values were then analysed using Analysis of Variance to determine the most contributing input parameter. On analysis it was found that peak current, flushing pressure and pulse-on time had an influence of 61.36%, 17.81% and 8.09% respectively on the optimal solution.

  12. Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms

    Institute of Scientific and Technical Information of China (English)

    ANDR(E)S-TORO B.; GIR(O)N-SIERRA J.M.; FERN(A)NDEZ-BLANCO P.; L(O)PEZ-OROZCO J.A.; BESADA-PORTAS E.

    2004-01-01

    This paper describes empirical research on the model, optimization and supervisory control of beer fermentation. Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results. The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs). Successful finding of optimal ways to drive these processes were reported. Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.

  13. SOME OPTIMALITY AND DUALITY RESULTS FOR AN EFFICIENT SOLUTION OF MULTIOBJECTIVE NONLINEAR FRACTIONAL PROGRAMMING PROBLEM

    Directory of Open Access Journals (Sweden)

    Paras Bhatnagar

    2012-10-01

    Full Text Available Kaul and Kaur [7] obtained necessary optimality conditions for a non-linear programming problem by taking the objective and constraint functions to be semilocally convex and their right differentials at a point to be lower semi-continuous. Suneja and Gupta [12] established the necessary optimality conditions without assuming the semilocal convexity of the objective and constraint functions but their right differentials at the optimal point to be convex. Suneja and Gupta [13] established necessary optimality conditions for an efficient solution of a multiobjective non-linear programming problem by taking the right differentials of the objective functions and constraintfunctions at the efficient point to be convex. In this paper we obtain some results for a properly efficient solution of a multiobjective non-linear fractional programming problem involving semilocally convex and related functions by assuming generalized Slater type constraint qualification.

  14. Multi-objective genetic algorithm for the optimization of a flat-plate solar thermal collector.

    Science.gov (United States)

    Mayer, Alexandre; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

    2014-10-20

    We present a multi-objective genetic algorithm we developed for the optimization of a flat-plate solar thermal collector. This collector consists of a waffle-shaped Al substrate with NiCrOx cermet and SnO(2) anti-reflection conformal coatings. Optimal geometrical parameters are determined in order to (i) maximize the solar absorptance α and (ii) minimize the thermal emittance ε. The multi-objective genetic algorithm eventually provides a whole set of Pareto-optimal solutions for the optimization of α and ε, which turn out to be competitive with record values found in the literature. In particular, a solution that enables α = 97.8% and ε = 4.8% was found.

  15. Multi-objective optimal design of active vibration absorber with delayed feedback

    Science.gov (United States)

    Huan, Rong-Hua; Chen, Long-Xiang; Sun, Jian-Qiao

    2015-03-01

    In this paper, a multi-objective optimal design of delayed feedback control of an actively tuned vibration absorber for a stochastically excited linear structure is investigated. The simple cell mapping (SCM) method is used to obtain solutions of the multi-objective optimization problem (MOP). The continuous time approximation (CTA) method is applied to analyze the delayed system. Stability is imposed as a constraint for MOP. Three conflicting objective functions including the peak frequency response, vibration energy of primary structure and control effort are considered. The Pareto set and Pareto front for the optimal feedback control design are presented for two examples. Numerical results have found that the Pareto optimal solutions provide effective delayed feedback control design.

  16. A Multiobjective Approach for the Heuristic Optimization of Compactness and Homogeneity in the Optimal Zoning

    Directory of Open Access Journals (Sweden)

    B. Bernábe-Loranca

    2012-06-01

    Full Text Available This paper presents a multiobjective methodology for optimal zoning design (OZ, based on the grouping ofgeographic data with characteristics of territorial aggregation. The two objectives considered are the minimization ofthe geometric compactness on the geographical location of the data and the homogeneity of any of the descriptivevariables. Since this problem is NP hard [1], our proposal provides an approximate solution taking into accountproperties of partitioning algorithms and design restrictions for territorial space. Approximate solutions are generatedthrough the set of optimum values (Maxima and the corresponding minimals (dual Minima [2] of the bi-objectivefunction using Variable Neighborhood Search (VNS [3] and the Pareto order defined over this set of values. Theresults obtained by our proposed approach constitute good solutions and are generated in a reasonably lowcomputational time.

  17. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

    Directory of Open Access Journals (Sweden)

    Qing-chun Meng

    Full Text Available CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

  18. Using Evolutionary Multiobjective Optimization Algorithms to Evolve Lacing Patterns for Bicycle Wheels

    OpenAIRE

    Svensson, Mats Krüger

    2015-01-01

    This thesis investigates the use of evolutionary algorithms (EAs) to evolve and optimize lacing patterns of spokes for a bicycle wheel. There are multiple objectives and tradeoffs to be considered when evaluating a lacing pattern, for instance, strength versus balance. To handle this, an evolutionary multiobjective optimization (EMO) method has been used. Various EMO algorithms and approaches are tested. Among these, the new NSGA-III algorithm is used. Different representations of the lac...

  19. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

    Science.gov (United States)

    Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

    2016-01-01

    CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

  20. The structure of weak Pareto solution sets in piecewise linear multiobjective optimization in normed spaces

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In general normed spaces,we consider a multiobjective piecewise linear optimization problem with the ordering cone being convex and having a nonempty interior.We establish that the weak Pareto optimal solution set of such a problem is the union of finitely many polyhedra and that this set is also arcwise connected under the cone convexity assumption of the objective function.Moreover,we provide necessary and suffcient conditions about the existence of weak(sharp) Pareto solutions.

  1. Multi-objective Truss Optimization Using Different Types of the BB-BC Algorithm

    Directory of Open Access Journals (Sweden)

    Milajić Aleksandar

    2016-01-01

    Full Text Available Optimum design of truss structures is considered as a benchmark problem in the field of the structural optimization. In order to solve this hard combinatorial problem, it is necessary to implement adequate optimization tool that would provide sufficiently wide range of possible solutions within a reasonable time as well as to obtain good exploration and exploitation of search space. The aim of presented study was to compare efficiency of different multi-objective algorithms in solving this task.

  2. Multiobjective waste management optimization strategy coupling life cycle assessment and genetic algorithms: application to PET bottles

    OpenAIRE

    Komly, Claude-Emma; Azzaro-Pantel, Catherine; Hubert, Antoine; Pibouleau, Luc; Archambault, Valérie

    2012-01-01

    International audience; A mathematical model based on life-cycle assessment (LCA) results is developed to assess the environmental efficiency of the end-of-life management of polyethylene terephthalate (PET) bottles. For this purpose, multiobjective optimization and decision support tools are used to define optimal targets for efficient waste management. The global environmental impacts associated with the treatment of PET bottles from their cradle to their ultimate graves (incineration, land...

  3. A New Non-dominated Sorting Genetic Algorithm for Multi-Objective Optimization

    OpenAIRE

    2010-01-01

    This study imitates the gene-therapy process at the forefront of medicine and proposes an innovative evaluative crossover operator. The evaluative crossover integrates a geneevaluation method with a gene-therapy approach in the traditional NSGA-II for finding uniformly distributed Pareto-optimal front of multi-objective optimization problems. To further enhance the advantages of fast non-dominate sorting and diversity preservation in NSGA-II, the proposed gene-evaluation method partially eval...

  4. Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

    Science.gov (United States)

    Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

    2017-01-01

    This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

  5. An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line

    Directory of Open Access Journals (Sweden)

    Zhengwu Fan

    2017-01-01

    Full Text Available In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO. Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature.

  6. A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables-Based Processes.

    Science.gov (United States)

    Helmdach, Daniel; Yaseneva, Polina; Heer, Parminder K; Schweidtmann, Artur M; Lapkin, Alexei A

    2017-09-22

    A decision support tool has been developed that uses global multiobjective optimization based on 1) the environmental impacts, evaluated within the framework of full life cycle assessment; and 2) process costs, evaluated by using rigorous process models. This approach is particularly useful in developing biorenewable-based energy solutions and chemicals manufacturing, for which multiple criteria must be evaluated and optimization-based decision-making processes are particularly attractive. The framework is demonstrated by using a case study of the conversion of terpenes derived from biowaste feedstocks into reactive intermediates. A two-step chemical conversion/separation sequence was implemented as a rigorous process model and combined with a life cycle model. A life cycle inventory for crude sulfate turpentine was developed, as well as a conceptual process of its separation into pure terpene feedstocks. The performed single- and multiobjective optimizations demonstrate the functionality of the optimization-based process development and illustrate the approach. The most significant advance is the ability to perform multiobjective global optimization, resulting in identification of a region of Pareto-optimal solutions. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Multiobjective Optimization for Fixture Locating Layout of Sheet Metal Part Using SVR and NSGA-II

    Directory of Open Access Journals (Sweden)

    Yuan Yang

    2017-01-01

    Full Text Available Fixture plays a significant role in determining the sheet metal part (SMP spatial position and restraining its excessive deformation in many manufacturing operations. However, it is still a difficult task to design and optimize SMP fixture locating layout at present because there exist multiple conflicting objectives and excessive computational cost of finite element analysis (FEA during the optimization process. To this end, a new multiobjective optimization method for SMP fixture locating layout is proposed in this paper based on the support vector regression (SVR surrogate model and the elitist nondominated sorting genetic algorithm (NSGA-II. By using ABAQUS™ Python script interface, a parametric FEA model is established. And the fixture locating layout is treated as design variables, while the overall deformation and maximum deformation of SMP under external forces are as the multiple objective functions. First, a limited number of training and testing samples are generated by combining Latin hypercube design (LHD with FEA. Second, two SVR prediction models corresponding to the multiple objectives are established by learning from the limited training samples and are integrated as the multiobjective optimization surrogate model. Third, NSGA-II is applied to determine the Pareto optimal solutions of SMP fixture locating layout. Finally, a multiobjective optimization for fixture locating layout of an aircraft fuselage skin case is conducted to illustrate and verify the proposed method.

  8. Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO)

    Science.gov (United States)

    Ghanei, A.; Assareh, E.; Biglari, M.; Ghanbarzadeh, A.; Noghrehabadi, A. R.

    2014-10-01

    Many studies are performed by researchers about shell and tube heat exchanger (STHE) but the multi-objective particle swarm optimization (PSO) technique has never been used in such studies. This paper presents application of thermal-economic multi-objective optimization of STHE using PSO. For optimal design of a STHE, it was first thermally modeled using e-number of transfer units method while Bell-Delaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. Multi objective PSO (MOPSO) method was applied to obtain the maximum effectiveness (heat recovery) and the minimum total cost as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called `Pareto optimal solutions'. In order to show the accuracy of the algorithm, a comparison is made with the non-dominated sorting genetic algorithm (NSGA-II) and MOPSO which are developed for the same problem.

  9. A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

    Science.gov (United States)

    Long, Kim Chenming

    Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this

  10. Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Aristeidis Antonakis

    2017-04-01

    Full Text Available In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.

  11. Multi-objective optimization problems concepts and self-adaptive parameters with mathematical and engineering applications

    CERN Document Server

    Lobato, Fran Sérgio

    2017-01-01

    This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.

  12. 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.

  13. Evolution strategies and multi-objective optimization of permanent magnet motor

    DEFF Research Database (Denmark)

    Andersen, Søren Bøgh; Santos, Ilmar

    2012-01-01

    of evolution strategies, ES to effectively design and optimize parameters of permanent magnet motors. Single as well as multi-objective optimization procedures are carried out. A modified way of creating the strategy parameters for the ES algorithm is also proposed and has together with the standard ES...... algorithm undergone a comprehensive parameter study for the parameters ρ and λ. The results of this parameter study show a significant improvement in stability and speed with the use of the modified ES version. To find the most effective selector for a multi-objective optimization, MOO, of the motor...... a performance examination of 4 different selectors from the group of programs called PISA has been made and compared for MOO of the efficiency and cost of the motor. This performance examination showed that the indicator based evolutionary algorithm, IBEA, and hypervolume estimation algorithm, HypE, selectors...

  14. A Novel Bioinspired Multiobjective Optimization Algorithm for Designing Wireless Sensor Networks in the Internet of Things

    Directory of Open Access Journals (Sweden)

    Jun Huang

    2015-01-01

    Full Text Available The design of wireless sensor networks (WSNs in the Internet of Things (IoT faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS. Unlike the ant colony optimization (ACO algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.

  15. Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm

    Science.gov (United States)

    Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi

    2012-03-01

    This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.

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

    Directory of Open Access Journals (Sweden)

    Sanjay Kr. Singh

    2014-05-01

    Full Text Available 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 not being connected to the grid; hence is usually used in refineries as steam turbine, where a hydraulic governor is used for the speed control. The PID controller for the system has been designed and implemented using MATLAB and SIMULINK and the results of the design methods have been compared, analysed and conclusions indicates that the significant improvement of results have been obtained by the Multi-Objective GA based optimization of PID as much faster response is obtained as compared to the ordinary GA and Ziegler Nichols method.

  17. CFD-based multi-objective optimization method for ship design

    Science.gov (United States)

    Tahara, Yusuke; Tohyama, Satoshi; Katsui, Tokihiro

    2006-10-01

    This paper concerns development and demonstration of a computational fluid dynamics (CFD)-based multi-objective optimization method for ship design. Three main components of the method, i.e. computer-aided design (CAD), CFD, and optimizer modules are functionally independent and replaceable. The CAD used in the present study is NAPA system, which is one of the leading CAD systems in ship design. The CFD method is FLOWPACK version 2004d, a Reynolds-averaged Navier-Stokes (RaNS) solver developed by the present authors. The CFD method is implemented into a self-propulsion simulator, where the RaNS solver is coupled with a propeller-performance program. In addition, a maneuvering simulation model is developed and applied to predict ship maneuverability performance. Two nonlinear optimization algorithms are used in the present study, i.e. the successive quadratic programming and the multi-objective genetic algorithm, while the former is mainly used to verify the results from the latter. For demonstration of the present method, a multi-objective optimization problem is formulated where ship propulsion and maneuverability performances are considered. That is, the aim is to simultaneously minimize opposite hydrodynamic performances in design tradeoff. In the following, an overview of the present method is given, and results are presented and discussed for tanker stern optimization problem including detailed verification work on the present numerical schemes.

  18. Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Congzhe; FANG Yuefa; GUO Sheng

    2015-01-01

    Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

  19. 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.

  20. Using Multi-Objective DEA to Assess the Overall and Partial Performance of Hierarchical Resource Utilization

    Directory of Open Access Journals (Sweden)

    Abdorrahman Haeri

    2013-02-01

    Full Text Available Heterogeneous resources transform into other resource forms through business processes and activities in organizations. This basic concept is called “resource transformation” in the literature. Resource transformation assumes that resources receive value from other resources and deliver value to the same resources. In this study, it is assumed that each resource acts as a Decision Making Unit (DMU that converts input factors from other resources to the output factors that are resources themselves. Multi-Objective Data Envelopment Analysis (MODEA is applied to attain overall and partial efficiency scores of resources to evaluate the performance of resources in utilizing different types of resources. The results show whether resources utilize other resources weakly or efficiently and provide the ability to compare the performance of different types of resource transformation. The findings help decision makers identify the weaknesses and strengths of resource performance in organizations.

  1. Design and optimization of pulsed Chemical Exchange Saturation Transfer MRI using a multiobjective genetic algorithm.

    Science.gov (United States)

    Yoshimaru, Eriko S; Randtke, Edward A; Pagel, Mark D; Cárdenas-Rodríguez, Julio

    2016-02-01

    Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners.

  2. A New Definition and Calculation Model for Evolutionary Multi-Objective Optimization

    Institute of Scientific and Technical Information of China (English)

    Zhou Ai-min; Kang Li-shan; Chen Yu-ping; Huang Yu-zhen

    2003-01-01

    We present a new definition (Evolving Solutions) for Multi objective Optimization Problem (MOP) to answer the basic question (what's multi-objective optimal solution?) and advance an asynchronous evolutionary model (MINT Model) to solve MOPs. The new theory is based on our understanding of the natural evolution and the analysis of the difference between natural evolution and MOP, thus it is not only different from the Converting Optimization but also different from Pareto Optimization.Some tests prove that our new theory may conquer disadvantages of the upper two methods to some extent.

  3. Multi-objective optimization of an insulating product based on wood fibre material

    Science.gov (United States)

    Hobballah, Mohamad; Vignon, Pierre; Tran, Huyen

    2016-10-01

    This article addresses the optimization of the quality of an insulating material that is based on wood fibres. In a context where several conflicting objectives must be satisfied simultaneously in the design process, meta-heuristic approaches provide efficient methods for optimization. Multi-objective particle swarm optimization (MOPSO) has been chosen here to solve this complex problem in which physical properties such as thermal conductivity and thickness recovery, that are conflicting, are modelled through heterogeneous variables and nonlinear mathematical models. This is an ongoing work; Influence graph and the first mathematical model are presented in this paper while the preliminary optimization results will be presented during the ESAFROM conference.

  4. Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes

    DEFF Research Database (Denmark)

    Maiti, Soumen K.; Eliasson Lantz, Anna; Bhushan, Mani

    2011-01-01

    as pareto optimal solutions. These solutions gives flexibility in evaluating the trade-offs and selecting the most suitable operating policy. Here, ε-constraint approach was used to generate the pareto solutions for two objectives: product concentration and product per unit cost of media, for batch and fed......Fermentation optimization involves potentially conflicting multiple objectives such as product concentration and production media cost. Simultaneous optimization of these objectives would result in a multiobjective optimization problem, which is characterized by a set of multiple solutions, knows...

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

    Directory of Open Access Journals (Sweden)

    Li Ran

    2017-01-01

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

  6. A Generalized Decision Framework Using Multi-objective Optimization for Water Resources Planning

    Science.gov (United States)

    Basdekas, L.; Stewart, N.; Triana, E.

    2013-12-01

    Colorado Springs Utilities (CSU) is currently engaged in an Integrated Water Resource Plan (IWRP) to address the complex planning scenarios, across multiple time scales, currently faced by CSU. The modeling framework developed for the IWRP uses a flexible data-centered Decision Support System (DSS) with a MODSIM-based modeling system to represent the operation of the current CSU raw water system coupled with a state-of-the-art multi-objective optimization algorithm. Three basic components are required for the framework, which can be implemented for planning horizons ranging from seasonal to interdecadal. First, a water resources system model is required that is capable of reasonable system simulation to resolve performance metrics at the appropriate temporal and spatial scales of interest. The system model should be an existing simulation model, or one developed during the planning process with stakeholders, so that 'buy-in' has already been achieved. Second, a hydrologic scenario tool(s) capable of generating a range of plausible inflows for the planning period of interest is required. This may include paleo informed or climate change informed sequences. Third, a multi-objective optimization model that can be wrapped around the system simulation model is required. The new generation of multi-objective optimization models do not require parameterization which greatly reduces problem complexity. Bridging the gap between research and practice will be evident as we use a case study from CSU's planning process to demonstrate this framework with specific competing water management objectives. Careful formulation of objective functions, choice of decision variables, and system constraints will be discussed. Rather than treating results as theoretically Pareto optimal in a planning process, we use the powerful multi-objective optimization models as tools to more efficiently and effectively move out of the inferior decision space. The use of this framework will help CSU

  7. Energy Efficiency - Spectral Efficiency Trade-off: A Multiobjective Optimization Approach

    KAUST Repository

    Amin, Osama

    2015-04-23

    In this paper, we consider the resource allocation problem for energy efficiency (EE) - spectral efficiency (SE) trade-off. Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and SE functions or change the priority level of each function using a trade-off parameter. Our dynamic approach is more tractable than the conventional approaches and more convenient to realistic communication applications and scenarios. We prove that the multiobjective optimization of the EE and SE is equivalent to a simple problem that maximizes the achievable rate/SE and minimizes the total power consumption. Then we apply the generalized framework of the resource allocation for the EE-SE trade-off to optimally allocate the subcarriers’ power for orthogonal frequency division multiplexing (OFDM) with imperfect channel estimation. Finally, we use numerical results to discuss the choice of the trade-off parameter and study the effect of the estimation error, transmission power budget and channel-to-noise ratio on the multiobjective optimization.

  8. Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2012-01-01

    Full Text Available In consideration of the significant role the brake plays in ensuring the fast and safe running of vehicles, and since the present parameter optimization design models of brake are far from the practical application, this paper proposes a multiobjective optimization model of drum brake, aiming at maximizing the braking efficiency and minimizing the volume and temperature rise of drum brake. As the commonly used optimization algorithms are of some deficiency, we present a differential evolution cellular multiobjective genetic algorithm (DECell by introducing differential evolution strategy into the canonical cellular genetic algorithm for tackling this problem. For DECell, the gained Pareto front could be as close as possible to the exact Pareto front, and also the diversity of nondominated individuals could be better maintained. The experiments on the test functions reveal that DECell is of good performance in solving high-dimension nonlinear multiobjective problems. And the results of optimizing the new brake model indicate that DECell obviously outperforms the compared popular algorithm NSGA-II concerning the number of obtained brake design parameter sets, the speed, and stability for finding them.

  9. 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.)

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

    Directory of Open Access Journals (Sweden)

    Jiuyuan Huo

    2017-02-01

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

  11. Multi-objective process parameter optimization for energy saving in injection molding process

    Institute of Scientific and Technical Information of China (English)

    Ning-yun LU; Gui-xia GONG; Yi YANG; Jian-hua LU

    2012-01-01

    This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi's method,a process analysis by analysis of variance (ANOVA),a process modeling algorithm by artificial neural network (ANN),and a multi-objective parameter optimization algorithm by genetic algorithm (GA)-based lexicographic method.Local and global Pareto analyses show the trade-off between product quality and energy consumption.The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests,and at the same time,the product quality can meet the pre-determined requirements.

  12. Design of homo-organic acid producing strains using multi-objective optimization

    DEFF Research Database (Denmark)

    Kim, Tae Yong; Park, Jong Myoung; Kim, Hyun Uk

    2015-01-01

    acids, while maintaining sufficiently high growth rate and minimizing the secretion of undesired byproducts. Homo-productions of acetic, lactic and succinic acids were targeted as examples. Engineered E. coli strains capable of producing homo-acetic and homo-lactic acids could be developed by taking...... this systems approach for the minimal identification of gene knockout targets. Also, failure to predict effective gene knockout targets for the homo-succinic acid production suggests that the multi-objective optimization is useful in assessing the suitability of a microorganism as a host strain......Production of homo-organic acids without byproducts is an important challenge in bioprocess engineering to minimize operation cost for separation processes. In this study, we used multi-objective optimization to design Escherichia coli strains with the goals of maximally producing target organic...

  13. Robust multi-objective optimization of state feedback controllers for heat exchanger system with probabilistic uncertainty

    Science.gov (United States)

    Lotfi, Babak; Wang, Qiuwang

    2013-07-01

    The performance of thermal control systems has, in recent years, improved in numerous ways due to developments in control theory and information technology. The shell-and-tube heat exchanger (STHX) is a medium where heat transfer process occurred. The accuracy of the heat exchanger depends on the performance of both elements. Therefore, both components need to be controlled in order to achieve a substantial result in the process. For this purpose, the actual dynamics of both shell and tube of the heat exchanger is crucial. In this paper, optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a STHX having parameters with probabilistic uncertainties. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for STHX problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic STHX using the conflicting objective functions in time domain. Such Pareto front is then obtained for STHX having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Hammersley Sequence Sampling (HSS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.

  14. A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

    OpenAIRE

    2010-01-01

    A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific p...

  15. [Multi-objectives optimization on life cycle pollutants emission of cassava-based ethanol blended gasoline fuels].

    Science.gov (United States)

    Pu, Geng-qiang; Hu, Zhi-yuan; Wang, Cheng-tao

    2004-09-01

    An optimization model on life cycle pollutants emission of cassava-based ethanol blended gasoline fuels, including single and multi-objectives, was carried out in this paper. And, the single and multi-objectives optimization of cassava-based ethanol blended gasoline fuels were done, using the life cycle CO, NOx, PM, HC, SOx, CO2 emissions as objectives. Moreover, sensitivity analysis of design variables was done. The multi-objectives results shown that the blend ratio between cassava-based ethanol and gasoline was 63%. Compare with the initial value, multi-objective optimization of cassava-based ethanol blended gasoline fuels achieved a little more life cycle CO, NOx and PM emissions, about 1%, 15% and 19% respectively, and reduced life cycle HC, SOx and CO2 emissions, 8%, 50%, and 21% respectively.

  16. Difficulties in Evolutionary Multiobjective Optimization for Many-Objective Optimization Problems and Their Scalability Improvement Techniques

    Science.gov (United States)

    Tsukamoto, Noritaka; Nojima, Yusuke; Ishibuchi, Hisao

    In this paper, we examine the behavior of evolutionary multiobjective optimization (EMO) algorithms to clarify the difficulties in their scalability to many-objective optimization problems. Whereas EMO algorithms usually work well on two-objective problems, it has also been reported that they do not work well on many-objective problems. First, we examine the behavior of the most well-known and frequently-used Pareto-based EMO algorithm (i. e. , NSGA-II) on many-objective 0/1 knapsack problems. Experimental results show that the search ability of NSGA-II is severely deteriorated by the increase in the number of objectives. This is because the selection pressure toward the Pareto front is severely weakened by the increase in the number of non-dominated solutions. Next we briefly review some approaches to the scalability improvement of EMO algorithms to many-objective problems. Then we examine their effects on the search ability of NSGA-II. Experimental results show that the improvement in the convergence of solutions to the Pareto front often leads to the decrease in their diversity.

  17. A comparative study of three simulation optimization algorithms for solving high dimensional multi-objective optimization problems in water resources

    Science.gov (United States)

    Schütze, Niels; Wöhling, Thomas; de Play, Michael

    2010-05-01

    Some real-world optimization problems in water resources have a high-dimensional space of decision variables and more than one objective function. In this work, we compare three general-purpose, multi-objective simulation optimization algorithms, namely NSGA-II, AMALGAM, and CMA-ES-MO when solving three real case Multi-objective Optimization Problems (MOPs): (i) a high-dimensional soil hydraulic parameter estimation problem; (ii) a multipurpose multi-reservoir operation problem; and (iii) a scheduling problem in deficit irrigation. We analyze the behaviour of the three algorithms on these test problems considering their formulations ranging from 40 up to 120 decision variables and 2 to 4 objectives. The computational effort required by each algorithm in order to reach the true Pareto front is also analyzed.

  18. Multi-Objective Configuration Optimization of a Hybrid Energy Storage System

    Directory of Open Access Journals (Sweden)

    Shan Cheng

    2017-02-01

    Full Text Available This study aims to investigate multi-objective configuration optimization of a hybrid energy storage system (HESS. In order to maximize the stability of the wind power output with minimized HESS investment, a multi-objective model for optimal HESS configuration has been established, which proposes decreasing the installation and operation & maintenance costs of an HESS and improving the compensation satisfaction rate of wind power fluctuation. Besides, fuzzy control has been used to allocate power in the HESS for lengthening battery lifetime and ensuring HESS with enough energy to compensate the fluctuation of the next time interval. Instead of converting multiple objectives into one, a multi-objective particle swarm optimization with integration of bacteria quorum sensing and circular elimination (BC-MOPSO has been applied to provide diverse alternative solutions. In order to illustrate the feasibility and effectiveness of the proposed model and the application of BC-MOPSO, simulations along with analysis and discussion are carried out. The results verified the feasibility and effectiveness of the proposed approach.

  19. Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Omprakash Kaiwartya

    2015-01-01

    Full Text Available A multiobjective dynamic vehicle routing problem (M-DVRP has been identified and a time seed based solution using particle swarm optimization (TS-PSO for M-DVRP has been proposed. M-DVRP considers five objectives, namely, geographical ranking of the request, customer ranking, service time, expected reachability time, and satisfaction level of the customers. The multiobjective function of M-DVRP has four components, namely, number of vehicles, expected reachability time, and profit and satisfaction level. Three constraints of the objective function are vehicle, capacity, and reachability. In TS-PSO, first of all, the problem is partitioned into smaller size DVRPs. Secondly, the time horizon of each smaller size DVRP is divided into time seeds and the problem is solved in each time seed using particle swarm optimization. The proposed solution has been simulated in ns-2 considering real road network of New Delhi, India, and results are compared with those obtained from genetic algorithm (GA simulations. The comparison confirms that TS-PSO optimizes the multiobjective function of the identified problem better than what is offered by GA solution.

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

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2014-01-01

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

  1. Multi-objectives fuzzy optimization model for ship form demonstration based on information entropy

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Selecting optimization ship form scheme is an important content in the process of concept design of ship. Multi-objective fuzzy decision-making model for ship form demonstration is set up according to the fuzzy pattern-recognition theory. Weight coefficients of each target of ship form scheme are determined by information entropy and individual subjective partiality. This model is used to select the optimal ship form scheme,the example shows that the model is exact and the result is credible. It can provide a reference for choosing the optimization scheme of ship form.

  2. Multi-objective genetic algorithm for the optimization of road surface cleaning process

    Institute of Scientific and Technical Information of China (English)

    CHEN Jie; GAO Dao-ming

    2006-01-01

    The parameters affecting road surface cleaning using waterjets were researched and a fuzzy neural network method of calculating cleaning rate was provided. A genetic algorithm was used to configure the cleaning parameters of pressure, standoff distance, traverse rate and angle of nozzles for the optimization of the cleaning effectiveness, efficiency, energy and water consumption, and a multi-objective optimization model was established. After calculation, the optimized results and the trend of variation of cleaning effectiveness, efficiency, energy and water consumption in different weighting factors were analyzed.

  3. Critical Comparison of Multi-objective Optimization Methods: Genetic Algorithms versus Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    V. Sedenka

    2010-09-01

    Full Text Available The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II and a novel multi-objective Particle Swarm Optimization (PSO. The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM. The hit rate and the quality of the Pareto front distribution are classified.

  4. Multi-objective behavioural mechanisms are adopted by foraging animals to achieve several optimality goals simultaneously.

    Science.gov (United States)

    Wajnberg, Eric

    2012-03-01

    1. Animals foraging for resources are under a variety of selective pressures, and separate optimality models have been developed predicting the optimal reproductive strategies they should adopt. 2. In most cases, the proximate behavioural mechanisms adopted to achieve such optimality goals have been identified. This is the case, for example, for optimal patch time and sex allocation in insect parasitoids. However, behaviours modelled within this framework have mainly been studied separately, even though real animals have to optimize some behaviours simultaneously. 3. For this reason, it would be better if proximate behavioural rules were designed to attain several goals simultaneously. Despite their importance, such multi-objective proximate rules remain to be discovered. 4. Based on experiments on insect parasitoids that simultaneously examine their optimal patch time and sex allocation strategies, it is shown here that animals can adopt multi-objective behavioural mechanisms that appear consistent with the two optimal goals simultaneously. 5. Results of computer simulations demonstrate that these behavioural mechanisms are indeed consistent with optimal reproductive strategies and have thus been most likely selected over the course of the evolutionary time.

  5. Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

    Science.gov (United States)

    Afshar, Puya; Brown, Martin; Maciejowski, Jan; Wang, Hong

    2011-12-01

    Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

  6. Optimal Design of Groundwater Remediation Problems under Uncertainty Using Probabilistic Multi-objective Evolutionary Technique

    Science.gov (United States)

    Yang, Y.; Wu, J.

    2011-12-01

    The previous work in the field of multi-objective optimization under uncertainty has concerned with the probabilistic multi-objective algorithm itself, how to effectively evaluate an estimate of uncertain objectives and identify a set of reliable Pareto optimal solutions. However, the design of a robust and reliable groundwater remediation system encounters major difficulties owing to the inherent uncertainty of hydrogeological parameters such as hydraulic conductivity (K). Thus, we need to make reduction of uncertainty associated with the site characteristics of the contaminated aquifers. In this study, we first use the Sequential Gaussian Simulation (SGSIM) to generate 1000 conditional realizations of lnK based on the sampled conditioning data acquired by field test. It is worthwhile to note that the cost for field test often weighs heavily upon the remediation cost and must thus be taken into account in the tradeoff between the solution reliability and remedial cost optimality. In this situation, we perform Monte Carlo simulation to make an uncertainty analysis of lnK realizations associated with the different number of conditioning data points. The results indicate that the uncertainty of the site characteristics and the contaminant concentration output from transport model is decreasing and then tends toward stabilization with the increase of conditioning data. This study presents a probabilistic multi-objective evolutionary algorithm (PMOEA) that integrates noisy genetic algorithm (NGA) and probabilistic multi-objective genetic algorithm (MOGA). The evident difference between deterministic MOGA and probabilistic MOGA is the use of probabilistic Pareto domination ranking and niche technique to ensure that each solution found is most reliable and robust. The proposed algorithm is then evaluated through a synthetic pump-and-treat (PAT) groundwater remediation test case. The 1000 lnK realizations generated by SGSIM with appropriate number of conditioning data (30

  7. MULTI-OBJECTIVE ONLINE OPTIMIZATION OF BEAM LIFETIME AT APS

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Yipeng

    2017-06-25

    In this paper, online optimization of beam lifetime at the APS (Advanced Photon Source) storage ring is presented. A general genetic algorithm (GA) is developed and employed for some online optimizations in the APS storage ring. Sextupole magnets in 40 sectors of the APS storage ring are employed as variables for the online nonlinear beam dynamics optimization. The algorithm employs several optimization objectives and is designed to run with topup mode or beam current decay mode. Up to 50\\% improvement of beam lifetime is demonstrated, without affecting the transverse beam sizes and other relevant parameters. In some cases, the top-up injection efficiency is also improved.

  8. A new multiobjective performance criterion used in PID tuning optimization algorithms.

    Science.gov (United States)

    Sahib, Mouayad A; Ahmed, Bestoun S

    2016-01-01

    In PID controller design, an optimization algorithm is commonly employed to search for the optimal controller parameters. The optimization algorithm is based on a specific performance criterion which is defined by an objective or cost function. To this end, different objective functions have been proposed in the literature to optimize the response of the controlled system. These functions include numerous weighted time and frequency domain variables. However, for an optimum desired response it is difficult to select the appropriate objective function or identify the best weight values required to optimize the PID controller design. This paper presents a new time domain performance criterion based on the multiobjective Pareto front solutions. The proposed objective function is tested in the PID controller design for an automatic voltage regulator system (AVR) application using particle swarm optimization algorithm. Simulation results show that the proposed performance criterion can highly improve the PID tuning optimization in comparison with traditional objective functions.

  9. Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision-Making

    Directory of Open Access Journals (Sweden)

    Meng-Dar Shieh

    2017-01-01

    Full Text Available Affective responses concern customers’ affective needs and have received increasing attention in consumer-focused research. To design a product that appeals to consumers, designers should consider multiple affective responses (MARs. Designing products capable of satisfying MARs falls into the category of multiobjective optimization (MOO. However, when exploring optimal product form design, most relevant studies have transformed multiple objectives into a single objective, which limits their usefulness to designers and consumers. To optimize product form design for MARs, this paper proposes an integrated model based on MOO and multicriteria decision-making (MCDM. First, design analysis is applied to identify design variables and MARs; quantification theory type I is then employed to build the relationship models between them; on the basis of these models, an MOO model for optimization of product form design is constructed. Next, we use nondominated sorting genetic algorithm-II (NSGA-II as a multiobjective evolutionary algorithm (MOEA to solve the MOO model and thereby derive Pareto optimal solutions. Finally, we adopt the fuzzy analytic hierarchy process (FAHP to obtain the optimal design from the Pareto solutions. A case study of car form design is conducted to demonstrate the proposed approach. The results suggest that this approach is feasible and effective in obtaining optimal designs and can provide great insight for product form design.

  10. Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

    Science.gov (United States)

    Kudryashov, Nikolay A.; Shilnikov, Kirill E.

    2016-06-01

    Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.

  11. Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

    Energy Technology Data Exchange (ETDEWEB)

    Kudryashov, Nikolay A.; Shilnikov, Kirill E. [National Research Nuclear University MEPhI, Department of Applied Mathematics, Moscow (Russian Federation)

    2016-06-08

    Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.

  12. Design for Sustainability of Industrial Symbiosis based on Emergy and Multi-objective Particle Swarm Optimization

    DEFF Research Database (Denmark)

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang

    2016-01-01

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative...... approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable...... performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied...

  13. Multiobjective Shape Optimization for Deployment and Adjustment Properties of Cable-Net of Deployable Antenna

    Directory of Open Access Journals (Sweden)

    Guoqiang You

    2015-01-01

    Full Text Available Based on structural features of cable-net of deployable antenna, a multiobjective shape optimization method is proposed to help to engineer antenna’s cable-net structure that has better deployment and adjustment properties. In this method, the multiobjective optimum mathematical model is built with lower nodes’ locations of cable-net as variables, the average stress ratio of cable elements and strain energy as objectives, and surface precision and natural frequency of cable-net as constraints. Sequential quadratic programming method is used to solve this nonlinear mathematical model in conditions with different weighting coefficients, and the results show the validity and effectiveness of the proposed method and model.

  14. Multi-objective evolutionary optimization of biological pest control with impulsive dynamics in soybean crops.

    Science.gov (United States)

    Cardoso, Rodrigo T N; da Cruz, André R; Wanner, Elizabeth F; Takahashi, Ricardo H C

    2009-08-01

    The biological pest control in agriculture, an environment-friendly practice, maintains the density of pests below an economic injury level by releasing a suitable quantity of their natural enemies. This work proposes a multi-objective numerical solution to biological pest control for soybean crops, considering both the cost of application of the control action and the cost of economic damages. The system model is nonlinear with impulsive control dynamics, in order to cope more effectively with the actual control action to be applied, which should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and trustworthy multi-objective genetic algorithm. The results suggest a dual pest control policy, in which the relative price of control action versus the associated additional harvest yield determines the usage of either a low control action strategy or a higher one.

  15. Multi-objective gene-pool optimal mixing evolutionary algorithms

    NARCIS (Netherlands)

    Luong, N.H.; La Poutré, J.A.; Bosman, P.A.N.; Igel, C.

    2014-01-01

    The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), with a lean, but sufficient, linkage model and an efficient variation operator, has been shown to be a robust and efficient methodology for solving single objective (SO) optimization problems with superior performance c

  16. Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks

    DEFF Research Database (Denmark)

    Bode, Felix; Binning, Philip John; Nowak, Wolfgang

    Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability and optim...

  17. Multiobjective sensitivity analysis and optimization of a distributed hydrologic model MOBIDIC

    Directory of Open Access Journals (Sweden)

    J. Yang

    2014-03-01

    Full Text Available Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives which arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for a distributed hydrologic model MOBIDIC, which combines two sensitivity analysis techniques (Morris method and State Dependent Parameter method with a multiobjective optimization (MOO approach ϵ-NSGAII. This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina with three objective functions, i.e., standardized root mean square error of logarithmic transformed discharge, water balance index, and mean absolute error of logarithmic transformed flow duration curve, and its results were compared with those with a single objective optimization (SOO with the traditional Nelder–Mead Simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show: (1 the two sensitivity analysis techniques are effective and efficient to determine the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization; (2 both MOO and SOO lead to acceptable simulations, e.g., for MOO, average Nash–Sutcliffe is 0.75 in the calibration period and 0.70 in the validation period; (3 evaporation and surface runoff shows similar importance to watershed water balance while the contribution of baseflow can be ignored; (4 compared to SOO which was dependent of initial starting location, MOO provides more insight on parameter sensitivity and conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and

  18. Multi-objective evolutionary optimization for greywater reuse in municipal sewer systems.

    Science.gov (United States)

    Penn, Roni; Friedler, Eran; Ostfeld, Avi

    2013-10-01

    Sustainable design and implementation of greywater reuse (GWR) has to achieve an optimum compromise between costs and potable water demand reduction. Studies show that GWR is an efficient tool for reducing potable water demand. This study presents a multi-objective optimization model for estimating the optimal distribution of different types of GWR homes in an existing municipal sewer system. Six types of GWR homes were examined. The model constrains the momentary wastewater (WW) velocity in the sewer pipes (which is responsible for solids movement). The objective functions in the optimization model are the total WW flow at the outlet of the neighborhoods sewer system and the cost of the on-site GWR treatment system. The optimization routing was achieved by an evolutionary multi-objective optimization coupled with hydrodynamic simulations of a representative sewer system of a neighborhood located at the coast of Israel. The two non-dominated best solutions selected were the ones having either the smallest WW flow discharged at the outlet of the neighborhood sewer system or the lowest daily cost. In both solutions most of the GWR types chosen were the types resulting with the smallest water usage. This lead to only a small difference between the two best solutions, regarding the diurnal patterns of the WW flows at the outlet of the neighborhood sewer system. However, in the upstream link a substantial difference was depicted between the diurnal patterns. This difference occurred since to the upstream links only few homes, implementing the same type of GWR, discharge their WW, and in each solution a different type of GWR was implemented in these upstream homes. To the best of our knowledge this is the first multi-objective optimization model aimed at quantitatively trading off the cost of local/onsite GW spatially distributed reuse treatments, and the total amount of WW flow discharged into the municipal sewer system under unsteady flow conditions.

  19. Method of Designing Missile Controller Based on Multi-Objective Optimization

    Institute of Scientific and Technical Information of China (English)

    LIN Bo; MENG Xiu-yun; LIU Zao-zhen

    2006-01-01

    A method of designing robust controller based on genetic algorithm is presented in order to overcome the drawback of manual modification and trial in designing the control system of missile. Specification functions which reflect the dynamic performance in time domain and robustness in frequency domain are presented,then dynamic/static performance, control cost and robust stability are incorporated into a multi-objective optimization problem. Genetic algorithm is used to solve the problem and achieve the optimal controller directly.Simulation results show that the controller provides a good stability and offers a good dynamic performance in a large flight envelope. The results also validate the effectiveness of the method.

  20. Multi-objective Design Optimization of a Parallel Schönflies-motion Robot

    DEFF Research Database (Denmark)

    Wu, Guanglei; Bai, Shaoping; Hjørnet, Preben

    2016-01-01

    This paper introduces a parallel Schoenflies-motion robot with rectangular workspace, which is suitable for pick-and-place operations. A multi-objective optimization problem is formulated to optimize the robot's geometric parameters with consideration of kinematic and dynamic performances....... The dynamic performance is concerned mainly the capability of force transmission in the parallel kinematic chain, for which transmission indices are defined. The Pareto-front is obtained to investigate the influence of the design variables to the robot performance. Dynamic characteristics for three Pareto...

  1. Gradient-based multiobjective optimization using a distance constraint technique and point replacement

    Science.gov (United States)

    Sato, Yuki; Izui, Kazuhiro; Yamada, Takayuki; Nishiwaki, Shinji

    2016-07-01

    This paper proposes techniques to improve the diversity of the searching points during the optimization process in an Aggregative Gradient-based Multiobjective Optimization (AGMO) method, so that well-distributed Pareto solutions are obtained. First to be discussed is a distance constraint technique, applied among searching points in the objective space when updating design variables, that maintains a minimum distance between the points. Next, a scheme is introduced that deals with updated points that violate the distance constraint, by deleting the offending points and introducing new points in areas of the objective space where searching points are sparsely distributed. Finally, the proposed method is applied to example problems to illustrate its effectiveness.

  2. Genetic algorithm as a tool for multi-objective optimization of permanent magnet disc motor

    Directory of Open Access Journals (Sweden)

    Cvetkovski Goga

    2016-06-01

    Full Text Available The analysed permanent magnet disc motor (PMDM is used for direct wheel drive in an electric vehicle. Therefore there are several objectives that could be tackled in the design procedure, such as an increased efficiency, reduced iron weight, reduced copper weight or reduced weight of the permanent magnets (reduced rotor weight. In this paper the optimal design of PMDM using a multi-objective genetic algorithm optimisation procedure is performed. A comparative analysis of the optimal motor solution and its parameters in relation to the prototype is presented.

  3. Multiobjective Optimal Control of Longitudinal Seismic Response of a Multitower Cable-Stayed Bridge

    Directory of Open Access Journals (Sweden)

    Geng Fangfang

    2016-01-01

    Full Text Available The dynamic behavior of a multitower cable-stayed bridge with the application of partially longitudinal constraint system using viscous fluid dampers under real earthquake ground motions is presented. The study is based on the dynamic finite element model of the Jiashao Bridge, a six-tower cable-stayed bridge in China. The prime aim of the study is to investigate the effectiveness of viscous fluid dampers on the longitudinal seismic responses of the bridge and put forth a multiobjective optimization design method to determine the optimized parameters of the viscous fluid dampers. The results of the investigations show that the control objective of the multitower cable-stayed bridge with the partially longitudinal constraint system is to yield maximum reductions in the base forces of bridge towers longitudinally restricted with the bridge deck, with slight increases in the base forces of bridge towers longitudinally unrestricted with the bridge deck. To this end, a multiobjective optimization design method that uses a nondominating sort genetic algorithm II (NSGA-II is used to optimize parameters of the viscous fluid dampers. The effectiveness of the proposed optimization design method is demonstrated for the multitower cable-stayed bridge with the partially longitudinal constraint system, which reveals that a design engineer can choose a set of proper parameters of the viscous fluid dampers from Pareto optimal fronts that can satisfy the desired performance requirements.

  4. Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System

    Directory of Open Access Journals (Sweden)

    Qiang Gao

    2013-01-01

    Full Text Available Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method.

  5. Composite multiobjective optimization beamforming based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Shi Jing; Meng Weixiao; Zhang Naitong; Wang Zheng

    2006-01-01

    All thc parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms (GAs).Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.

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

    Directory of Open Access Journals (Sweden)

    MICHAEL J. LEE

    2016-01-01

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

  7. Multi-objective trajectory optimization for the space exploration vehicle

    Science.gov (United States)

    Qin, Xiaoli; Xiao, Zhen

    2016-07-01

    The research determines temperature-constrained optimal trajectory for the space exploration vehicle by developing an optimal control formulation and solving it using a variable order quadrature collocation method with a Non-linear Programming(NLP) solver. The vehicle is assumed to be the space reconnaissance aircraft that has specified takeoff/landing locations, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom aircraft model is adapted from previous work and includes flight dynamics, and thermal constraints.Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and exploration of space targets. In addition, the vehicle models include the environmental models(gravity and atmosphere). How these models are appropriately employed is key to gaining confidence in the results and conclusions of the research. Optimal trajectories are developed using several performance costs in the optimal control formation,minimum time,minimum time with control penalties,and maximum distance.The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for large-scale space exloration.

  8. Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    Shan CHENG; Min-you CHEN; Rong-jong WAI; Fang-zong WANG

    2014-01-01

    This paper deals with the optimal placement of distributed generation (DG) units in distribution systems via an enhanced multi-objective particle swarm optimization (EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational con-straints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been inte-grated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is em-ployed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage sta-bility. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and loca-tions of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.

  9. Multi-objective optimization framework for networked predictive controller design.

    Science.gov (United States)

    Das, Sourav; Das, Saptarshi; Pan, Indranil

    2013-01-01

    Networked Control Systems (NCSs) often suffer from random packet dropouts which deteriorate overall system's stability and performance. To handle the ill effects of random packet losses in feedback control systems, closed over communication network, a state feedback controller with predictive gains has been designed. To achieve improved performance, an optimization based controller design framework has been proposed in this paper with Linear Matrix Inequality (LMI) constraints, to ensure guaranteed stability. Different conflicting objective functions have been optimized with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The methodology proposed in this paper not only gives guaranteed closed loop stability in the sense of Lyapunov, even in the presence of random packet losses, but also gives an optimization trade-off between two conflicting time domain control objectives.

  10. Multiobjective optimization for nuclear fleet evolution scenarios using COSI

    Directory of Open Access Journals (Sweden)

    Freynet David

    2016-01-01

    Full Text Available The consequences of various fleet evolution options on material inventories and flux in fuel cycle and waste can be analysed by means of transition scenario studies. The COSI code is currently simulating chronologically scenarios whose parameters are fully defined by the user and is coupled with the CESAR depletion code. As the interactions among reactors and fuel cycle facilities can be complex, and the ways in which they may be configured are many, the development of optimization methodology could improve scenario studies. The optimization problem definition needs to list: (i criteria (e.g. saving natural resources and minimizing waste production; (ii variables (scenario parameters related to reprocessing, reactor operation, installed power distribution, etc.; (iii constraints making scenarios industrially feasible. The large number of scenario calculations needed to solve an optimization problem can be time-consuming and hardly achievable; therefore, it requires the shortening of the COSI computation time. Given that CESAR depletion calculations represent about 95% of this computation time, CESAR surrogate models have been developed and coupled with COSI. Different regression models are compared to estimate CESAR outputs: first- and second-order polynomial regressions, Gaussian process and artificial neural network. This paper is about a first optimization study of a transition scenario from the current French nuclear fleet to a Sodium Fast Reactors fleet as defined in the frame of the 2006 French Act for waste management. The present article deals with obtaining the optimal scenarios and validating the methodology implemented, i.e. the coupling between the simulation software COSI, depletion surrogate models and a genetic algorithm optimization method.

  11. Environmental multi-objective optimization of the use of biomass resources for energy.

    Science.gov (United States)

    Vadenbo, Carl; Tonini, Davide; Astrup, Thomas Fruergaard

    2017-02-17

    Bioenergy is often considered an important component, alongside other renewables, to mitigate global warming and to reduce fossil fuel dependency. Determining sustainable strategies for utilizing biomass resources, however, requires a holistic perspective to reflect a wider range of potential environmental consequences. To circumvent the limitations of scenario-based life cycle assessment (LCA), we develop a multi-objective optimization model to systematically identify the environmentally-optimal use of biomass for energy under given system constraints. Besides satisfying annual final energy demand, the model constraints comprise availability of biomass and arable land, technology- and system-specific capacities, and relevant policy targets. Efficiencies and environmental performances of bioenergy conversions are derived using biochemical process models combined with LCA data. The application of the optimization model is exemplified by a case aimed at determining the environmentally-optimal use of biomass in the Danish energy system in 2025. A multi-objective formulation based on fuzzy intervals for six environmental impact categories resulted in impact reductions of 13-43% compared to the baseline. The robustness of the optimal solution was analyzed with respect to parameter uncertainty and choice of environmental objectives.

  12. Multiobjective binary biogeography based optimization for feature selection using gene expression data.

    Science.gov (United States)

    Li, Xiangtao; Yin, Minghao

    2013-12-01

    Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.

  13. Multi-Objective Optimization of Two-Stage Helical Gear Train Using NSGA-II

    Directory of Open Access Journals (Sweden)

    R. C. Sanghvi

    2014-01-01

    Full Text Available Gears not only transmit the motion and power satisfactorily but also can do so with uniform motion. The design of gears requires an iterative approach to optimize the design parameters that take care of kinematics aspects as well as strength aspects. Moreover, the choice of materials available for gears is limited. Owing to the complex combinations of the above facts, manual design of gears is complicated and time consuming. In this paper, the volume and load carrying capacity are optimized. Three different methodologies (i MATLAB optimization toolbox, (ii genetic algorithm (GA, and (iii multiobjective optimization (NSGA-II technique are used to solve the problem. In the first two methods, volume is minimized in the first step and then the load carrying capacities of both shafts are calculated. In the third method, the problem is treated as a multiobjective problem. For the optimization purpose, face width, module, and number of teeth are taken as design variables. Constraints are imposed on bending strength, surface fatigue strength, and interference. It is apparent from the comparison of results that the result obtained by NSGA-II is more superior than the results obtained by other methods in terms of both objectives.

  14. Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics

    Directory of Open Access Journals (Sweden)

    Mirko M. Stojiljković

    2014-12-01

    Full Text Available In this paper, a methodology for multi-objective optimization of trigeneration plants is presented. It is primarily applicable to the systems for buildings’ energy supply characterized by high load variations on daily, weekly and annual bases, as well as the components applicable for flexible operation. The idea is that this approach should enable high accuracy and flexibility in mathematical modeling, while remaining efficient enough. The optimization problem is structurally decomposed into two new problems. The main problem of synthesis and design optimization is combinatorial and solved with different metaheuristic methods. For each examined combination of the synthesis and design variables, when calculating the values of the objective functions, the inner, mixed integer linear programming operation optimization problem is solved with the branch-and-cut method. The applicability of the exploited metaheuristic methods is demonstrated. This approach is compared with the alternative, superstructure-based approach. The potential for combining them is also examined. The methodology is applied for multi-objective optimization of a trigeneration plant that could be used for the energy supply of a real residential settlement in Niš, Serbia. Here, two objectives are considered: annual total costs and primary energy consumption. Results are obtained in the form of a Pareto chart using the epsilon-constraint method.

  15. 3D Pattern Synthesis of Time-Modulated Conformal Arrays with a Multiobjective Optimization Approach

    Directory of Open Access Journals (Sweden)

    Wentao Li

    2014-01-01

    Full Text Available This paper addresses the synthesis of the three-dimensional (3D radiation patterns of the time-modulated conformal arrays. Due to the nature of periodic time modulation, harmonic radiation patterns are generated at the multiples of the modulation frequency in time-modulated arrays. Thus, the optimization goal of the time-modulated conformal array includes the optimization of the sidelobe level at the operating frequency and the sideband levels (SBLs at the harmonic frequency, and the design can be regarded as a multiobjective problem. The multiobjective particle swarm optimization (MOPSO is applied to optimize the switch-on instants and pulse durations of the time-modulated conformal array. To significantly reduce the optimization variables, the modified Bernstein polynomial is employed in the synthesis process. Furthermore, dual polarized patch antenna is designed as radiator to achieve low cross-polarization level during the beam scanning. A 12 × 13 (156-element conical conformal microstrip array is simulated to demonstrate the proposed synthesis mechanism, and good results reveal the promising ability of the proposed algorithm in solving the synthesis of the time-modulated conformal arrays problem.

  16. Multiobjective Optimization of a Counterrotating Type Pump-Turbine Unit Operated at Turbine Mode

    Directory of Open Access Journals (Sweden)

    Jin-Hyuk Kim

    2014-05-01

    Full Text Available A multiobjective optimization for improving the turbine output and efficiency of a counterrotating type pump-turbine unit operated at turbine mode was carried out in this work. The blade geometry of both the runners was optimized using a hybrid multiobjective evolutionary algorithm coupled with a surrogate model. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model were discretized by finite volume approximations and solved on hexahedral grids to analyze the flow in the pump-turbine unit. As major hydrodynamic performance parameters, the turbine output and efficiency were selected as objective functions with two design variables related to the hub profiles of both the runner blades. These objectives were numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. Response surface approximation models for the objectives were constructed based on the objective function values at the design points. A fast nondominated sorting genetic algorithm for the local search coupled with the response surface approximation models was applied to determine the global Pareto-optimal solutions. The trade-off between the two objectives was determined and described with respect to the Pareto-optimal solutions. The results of this work showed that the turbine outputs and efficiencies of optimized pump-turbine units were simultaneously improved in comparison to the reference unit.

  17. Multi-Objective Optimization and Multi-Model Analysis of Watershed Management Under Uncertainty

    Science.gov (United States)

    Shoemaker, C. A.; Akhtar, T.; Woodbury, J.

    2010-12-01

    Watershed Management planning can be assisted by the use of models that can incorporate the effect of management practices on hydrology and pollution transport under the effects of stochastic weather, including weather patterns influenced by climate change. However, such analysis is based usually on only one model (a set of equations) and the calibration of the model’s parameters to data. In this analysis we will discuss the use of two new multiobjective optimization methods for the incorporation of multiple criteria into choice of calibrated parameter values. One of these multiobjective methods (using radial basis functions) has been developed by our group, and a second new method from another group is based on Kriging. In addition we will compare these two new methods to the results obtained by the older (and widely used) NSGA-II multi-objective method on watershed models. We have developed two models and applied them to a large (1200 km2) northeastern watershed. The first model is based on SWAT2005, and the second model replaces SWAT’s Hortonian hydrology with variable source area (VSA) hydrology. In actuality a watershed’s flow paths can be expected to vary between Hortonian and VSA hydrology under different weather conditions. We present a multi-model analysis using Bayesian Model Averaging of these two types of models to obtain an improved estimate of the effects of alternative phosphorous management practices on long term sustainability of water quality in the watershed under a wide range of weather scenarios.

  18. Bio Inspired Algorithms in Single and Multiobjective Reliability Optimization

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albeanu, Grigore; Burtschy, Bernard

    2014-01-01

    Non-traditional search and optimization methods based on natural phenomena have been proposed recently in order to avoid local or unstable behavior when run towards an optimum state. This paper describes the principles of bio inspired algorithms and reports on Migration Algorithms and Bees...

  19. Steepest-Ascent Constrained Simultaneous Perturbation for Multiobjective Optimization

    DEFF Research Database (Denmark)

    McClary, Dan; Syrotiuk, Violet; Kulahci, Murat

    2011-01-01

    that leverages information about the known gradient to constrain the perturbations used to approximate the others. We apply SP(SA)(2) to the cross-layer optimization of throughput, packet loss, and end-to-end delay in a mobile ad hoc network (MANET), a self-organizing wireless network. The results show that SP...

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

    Directory of Open Access Journals (Sweden)

    Darius Mačiūnas

    2012-01-01

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

  1. Multiobjective Network Optimization for Soil Monitoring of the Loess Hilly Region in China

    Directory of Open Access Journals (Sweden)

    Dianfeng Liu

    2014-01-01

    Full Text Available The soil monitoring network plays an important role in detecting the spatial distribution of soil attributes and facilitates sustainable land-use decision making. Reduced costs, higher speed, greater scope, and a loss of accuracy are necessary to design a regional monitoring network effectively. In this paper, we present a stochastic optimization design method for regional soil carbon and water content monitoring networks with a minimum sample size based on a modified particle swarm optimization algorithm equipped with multiobjective optimization technique. Our effort is to reconcile the conflicts between various objectives, that is, kriging variance, survey budget, spatial accessibility, spatial interval, and the amount of monitoring sites. We applied the method to optimize the soil monitoring networks in a semiarid loess hilly area located in northwest China. The results reveal that the proposed method is both effective and robust and outperforms the standard binary particle swarm optimization and spatial simulated annealing algorithm.

  2. Anatomy-based three-dimensional dose optimization in brachytherapy using multiobjective genetic algorithms.

    Science.gov (United States)

    Lahanas, M; Baltas, D; Zamboglou, N

    1999-09-01

    In conventional dose optimization algorithms, in brachytherapy, multiple objectives are expressed in terms of an aggregating function which combines individual objective values into a single utility value, making the problem single objective, prior to optimization. A multiobjective genetic algorithm (MOGA) was developed for dose optimization based on an a posteriori approach, leaving the decision-making process to a planner and offering a representative trade-off surface of the various objectives. The MOGA provides a flexible search engine which provides the maximum of information for a decision maker. Tests performed with various treatment plans in brachytherapy have shown that MOGA gives solutions which are superior to those of traditional dose optimization algorithms. Objectives were proposed in terms of the COIN distribution and differential volume histograms, taking into account patient anatomy in the optimization process.

  3. Open- and closed-loop multiobjective optimal strategies for HIV therapy using NSGA-II.

    Science.gov (United States)

    Heris, S Mostapha Kalami; Khaloozadeh, Hamid

    2011-06-01

    In this paper, multiobjective open- and closed-loop optimal treatment strategies for HIV/AIDS are presented. It is assumed that highly active antiretroviral therapy is available for treatment of HIV infection. Amount of drug usage and the quality of treatment are defined as two objectives of a biobjective optimization problem, and Nondominated Sorting Genetic Algorithm II is used to solve this problem. Open- and closed-loop control strategies are used to produce optimal control inputs, and the Pareto frontiers obtained from these two strategies are compared. Pareto frontier, resulted from the optimization process, suggests a set of treatment strategies, which all are optimal from a perspective, and can be used in different medical and economic conditions. Robustness of closed-loop system in the presence of measurement noises is analyzed, assuming various levels of noise.

  4. Fatigue design of a cellular phone folder using regression model-based multi-objective optimization

    Science.gov (United States)

    Kim, Young Gyun; Lee, Jongsoo

    2016-08-01

    In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.

  5. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    Science.gov (United States)

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  6. Determination of an optimal control strategy for drug administration in tumor treatment using multi-objective optimization differential evolution.

    Science.gov (United States)

    Lobato, Fran Sérgio; Machado, Vinicius Silvério; Steffen, Valder

    2016-07-01

    The mathematical modeling of physical and biologic systems represents an interesting alternative to study the behavior of these phenomena. In this context, the development of mathematical models to simulate the dynamic behavior of tumors is configured as an important theme in the current days. Among the advantages resulting from using these models is their application to optimization and inverse problem approaches. Traditionally, the formulated Optimal Control Problem (OCP) has the objective of minimizing the size of tumor cells by the end of the treatment. In this case an important aspect is not considered, namely, the optimal concentrations of drugs may affect the patients' health significantly. In this sense, the present work has the objective of obtaining an optimal protocol for drug administration to patients with cancer, through the minimization of both the cancerous cells concentration and the prescribed drug concentration. The resolution of this multi-objective problem is obtained through the Multi-objective Optimization Differential Evolution (MODE) algorithm. The Pareto's Curve obtained supplies a set of optimal protocols from which an optimal strategy for drug administration can be chosen, according to a given criterion.

  7. 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

    2016-07-01

    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.

  8. 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.

  9. Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method & Grey relational analysis

    Science.gov (United States)

    Shivade, Anand S.; Shinde, Vasudev D.

    2014-09-01

    In this paper, wire electrical discharge machining of D3 tool steel is studied. Influence of pulse-on time, pulse-off time, peak current and wire speed are investigated for MRR, dimensional deviation, gap current and machining time, during intricate machining of D3 tool steel. Taguchi method is used for single characteristics optimization and to optimize all four process parameters simultaneously, Grey relational analysis (GRA) is employed along with Taguchi method. Through GRA, grey relational grade is used as a performance index to determine the optimal setting of process parameters for multi-objective characteristics. Analysis of variance (ANOVA) shows that the peak current is the most significant parameters affecting on multi-objective characteristics. Confirmatory results, proves the potential of GRA to optimize process parameters successfully for multi-objective characteristics.

  10. Combining support vector regression and cellular genetic algorithm for multi-objective optimization of coal-fired utility boilers

    Energy Technology Data Exchange (ETDEWEB)

    Feng Wu; Hao Zhou; Tao Ren; Ligang Zheng; Kefa Cen [Zhejiang University, Hangzhou (China). State Key Laboratory of Clean Energy Utilization

    2009-10-15

    Support vector regression (SVR) was employed to establish mathematical models for the NOx emissions and carbon burnout of a 300 MW coal-fired utility boiler. Combined with the SVR models, the cellular genetic algorithm for multi-objective optimization (MOCell) was used for multi-objective optimization of the boiler combustion. Meanwhile, the comparison between MOCell and the improved non-dominated sorting genetic algorithm (NSGA-II) shows that MOCell has superior performance to NSGA-II regarding the problem. The field experiments were carried out to verify the accuracy of the results obtained by MOCell, the results were in good agreement with the measurement data. The proposed approach provides an effective tool for multi-objective optimization of coal combustion performance, whose feasibility and validity are experimental validated. A time period of less than 4 s was required for a run of optimization under a PC system, which is suitable for the online application. 19 refs., 8 figs., 2 tabs.

  11. Multi-objective optimization of process based on resource capability

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To improve the practicability, suitability and accuracy of the trade-off among time, cost and quality of a process, a method based on resource capability is introduced. Through analyzing the relationship between an activity and its' supporting resource, the model trades off the time, cost and quality by changing intensity of labor or changing the types of supporting resource or units of labor of resource in a certain time respectively according to the different types of its' supporting resources. Through contrasting this method with the model of unit time cost corresponding to different quality levels and inter-related linear programming model of time, cost and quality for process optimizing, it is shown that this model does not only cover the above two models but also can describe some conditions the above two models can not express. The method supports to select different function to optimize a process according to different types of its supporting resource.

  12. Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

    Science.gov (United States)

    Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

    2005-01-01

    An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

  13. Deployment of Wireless Sensor Networks for Oilfield Monitoring by Multiobjective Discrete Binary Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Zhen-Lun Yang

    2016-01-01

    Full Text Available The deployment problem of wireless sensor networks for real time oilfield monitoring is studied. As a characteristic of oilfield monitoring system, all sensor nodes have to be installed on designated spots. For the energy efficiency, some relay nodes and sink nodes are deployed as a delivery subsystem. The major concern of the construction of the monitoring system is the optimum placement of data delivery subsystem to ensure the full connectivity of the sensor nodes while keeping the construction cost as low as possible, with least construction and maintenance complexity. Due to the complicated landform of oilfields, in general, it is rather difficult to satisfy these requirements simultaneously. The deployment problem is formulated as a constrained multiobjective optimization problem and solved through a novel scheme based on multiobjective discrete binary particle swarm optimization to produce optimal solutions from the minimum financial cost to the minimum complexity of construction and maintenance. Simulation results validated that comparing to the three existing state-of-the-art algorithms, that is, NSGA-II, JGGA, and SPEA2, the proposed scheme is superior in locating the Pareto-optimal front and maintaining the diversity of the solutions, thus providing superior candidate solutions for the design of real time monitoring systems in oilfields.

  14. Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management

    Science.gov (United States)

    Kourakos, George; Mantoglou, Aristotelis

    2013-02-01

    SummaryThe demand for fresh water in coastal areas and islands can be very high due to increased local needs and tourism. A multi-objective optimization methodology is developed, involving minimization of economic and environmental costs while satisfying water demand. The methodology considers desalinization of pumped water and injection of treated water into the aquifer. Variable density aquifer models 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(MNNs)]. The surrogate models are trained adaptively during optimization based on a genetic algorithm. In the crossover step, each pair of parents generates a pool of offspring which are evaluated using the fast surrogate model. Then, the most promising offspring are evaluated using the exact numerical model. This procedure eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. The method has important advancements compared to previous methods such as precise evaluation of the Pareto set and alleviation of propagation of errors due to surrogate model approximations. The method is applied to an aquifer in the Greek island of Santorini. The results show that the new MOSA(MNN) algorithm offers significant reduction in computational time compared to previous methods (in the case study it requires only 5% of the time required by other methods). Further, the Pareto solution is better than the solution obtained by alternative algorithms.

  15. Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods

    Directory of Open Access Journals (Sweden)

    B. Y. Qu

    2017-01-01

    Full Text Available Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.

  16. Multi-objective Genetic Algorithm for System Identification and Controller Optimization of Automated Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Xing Wu

    2011-07-01

    Full Text Available This paper presents a multi-objective genetic algorithm (MOGA with Pareto optimality and elitist tactics for the control system design of automated guided vehicle (AGV. The MOGA is used to identify AGV driving system model and optimize its servo control system sequentially. In system identification, the model identified by least square method is adopted as an evolution tutor who selects the individuals having balanced performances in all objectives as elitists. In controller optimization, the velocity regulating capability required by AGV path tracking is employed as decision-making preferences which select Pareto optimal solutions as elitists. According to different objectives and elitist tactics, several sub-populations are constructed and they evolve concurrently by using independent reproduction, neighborhood mutation and heuristic crossover. The lossless finite precision method and the multi-objective normalized increment distance are proposed to keep the population diversity with a low computational complexity. Experiment results show that the cascaded MOGA have the capability to make the system model consistent with AGV driving system both in amplitude and phase, and to make its servo control system satisfy the requirements on dynamic performance and steady-state accuracy in AGV path tracking.

  17. An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

    Science.gov (United States)

    Jiang, Shouyong; Yang, Shengxiang

    2016-02-01

    The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

  18. A Global Multi-Objective Optimization Tool for Design of Mechatronic Components using Generalized Differential Evolution

    DEFF Research Database (Denmark)

    Bech, Michael Møller; Nørgård, Christian; Roemer, Daniel Beck

    2016-01-01

    This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri-objectiv...... different optimization control parameter settings and it is concluded that GDE3 is a reliable optimization tool that can assist mechatronic engineers in the design and decision making process.......This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri......-objective problems having 10+ design variables are both highly constrained, nonlinear and non-smooth but nevertheless the algorithm converges to the Pareto-front within a hours of computation (20k function evaluations). Additionally, the robustness and convergence speed of the algorithm are investigated using...

  19. A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

    Directory of Open Access Journals (Sweden)

    Yalin Wang

    2013-01-01

    Full Text Available The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS, the satisfactory solution is obtained by using a decision-making method for multiple attributes.

  20. An effective docking strategy for virtual screening based on multi-objective optimization algorithm

    Directory of Open Access Journals (Sweden)

    Kang Ling

    2009-02-01

    Full Text Available Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.

  1. Load Sharing Multiobjective Optimization Design of a Split Torque Helicopter Transmission

    Directory of Open Access Journals (Sweden)

    Chenxi Fu

    2015-01-01

    Full Text Available Split torque designs can offer significant advantages over the traditional planetary designs for helicopter transmissions. However, it has two unique properties, gap and phase differences, which result in the risk of unequal load sharing. Various methods have been proposed to eliminate the effect of gap and promote load sharing to a certain extent. In this paper, system design parameters will be optimized to change the phase difference, thereby further improving load sharing. A nonlinear dynamic model is established to measure the load sharing with dynamic mesh forces quantitatively. Afterwards, a multiobjective optimization of a reference split torque design is conducted with the promoting of load sharing property, lightweight, and safety considered as the objectives. The load sharing property, which is measured by load sharing coefficient, is evaluated under multiple operating conditions with dynamic analysis method. To solve the multiobjective model with NSGA-II, an improvement is done to overcome the problem of time consuming. Finally, a satisfied optimal solution is picked up as the final design from the Pareto optimal front, which achieves improvements in all the three objectives compared with the reference design.

  2. Interactive Evolutionary Multi-Objective Optimization Algorithm Using Cone Dominance

    Institute of Scientific and Technical Information of China (English)

    Dalaijargal Purevsuren; Saif ur Rehman; Gang Cui; Jianmin Bao; Nwe Nwe Htay Win

    2015-01-01

    As the number of objectives increases, the performance of the Pareto dominance⁃based Evolutionary Multi⁃objective Optimization ( EMO) algorithms such as NSGA⁃II, SPEA2 severely deteriorates due to the drastic increase in the Pareto⁃incomparable solutions. We propose a sorting method which classifies these incomparable solutions into several ordered classes by using the decision maker's ( DM) preference information. This is accomplished by designing an interactive evolutionary algorithm and constructing convex cones. This method allows the DMs to drive the search process toward a preferred region of the Pareto optimal front. The performance of the proposed algorithm is assessed for two, three, and four⁃objective knapsack problems. The results demonstrate the algorithm's ability to converge to the most preferred point. The evaluation and comparison of the results indicate that the proposed approach gives better solutions than that of NSGA⁃II. In addition, the approach is more efficient compared to NSGA⁃II in terms of the number of generations required to reach the preferred point.

  3. Interactive Multi-objective Optimization Design for the Pylon Structure of an Airplane

    Institute of Scientific and Technical Information of China (English)

    An Weigang; Li Weiji

    2007-01-01

    The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will be unacceptable in engineering practice due to the large amount of evaluation needed for the algorithm. So, a new interactive optimization algorithm-interactive multi-objective particle swarm optimization (IMOPSO) is presented. IMOPSO is efficient, simple and operable. The decision-maker can expediently determine the accurate preference in IMOPSO. IMOPSO is used to perform the pylon structure optimization design of an airplane, and a satisfactory design is achieved after only 12 generations of IMOPSO evolutions. Compared with original design, the maximum displacement of the satisfactory design is reduced, and the mass of the satisfactory design is decreased for 22%.

  4. Power System Stabilizer Design Based on a Particle Swarm Optimization Multiobjective Function Implemented Under Graphical Interface

    Directory of Open Access Journals (Sweden)

    Ghouraf Djamel Eddine

    2016-05-01

    Full Text Available Power system stability considered a necessary condition for normal functioning of an electrical network. The role of regulation and control systems is to ensure that stability by determining the essential elements that influence it. This paper proposes a Particle Swarm Optimization (PSO based multiobjective function to tuning optimal parameters of Power System Stabilizer (PSS; this later is used as auxiliary to generator excitation system in order to damp electro mechanicals oscillations of the rotor and consequently improve Power system stability. The computer simulation results obtained by developed graphical user interface (GUI have proved the efficiency of PSS optimized by a Particle Swarm Optimization, in comparison with a conventional PSS, showing stable   system   responses   almost   insensitive   to   large parameter variations.Our present study was performed using a GUI realized under MATLAB in our work.

  5. Multi-objective optimization of circular magnetic abrasive polishing of SUS304 and Cu materials

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, NhatTan; Yin, ShaoHui; Chen, FengJun; Yin, HanFeng [Hunan University, Changsha (China); Pham, VanThoan [Hanoi University, Hanoi (Viet Nam); Tran, TrongNhan [Industrial University of Ho Chi Minh City, HCM City (Viet Nam)

    2016-06-15

    In this paper, a Multi-objective particle swarm optimization algorithm (MOPSOA) is applied to optimize surface roughness of workpiece after circular magnetic abrasive polishing. The most important parameters of polishing model, namely current, gap between pole and workpiece, spindle speed and polishing time, were considered in this approach. The objective functions of the MOPSOA depend on the quality of surface roughness of polishing materials with both simultaneous surfaces (Ra1, Ra2), which are determined by means of experimental approach with the aid of circular magnetic field. Finally, the effectiveness of the approach is compared between the optimal results with the experimental data. The results show that the new proposed polishing optimization method is more feasible.

  6. 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.

  7. A MODIFIED INVASIVE WEED OPTIMIZATION ALGORITHM FOR MULTIOBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEMS

    Directory of Open Access Journals (Sweden)

    Souad Mekni

    2014-11-01

    Full Text Available In this paper, a modified invasive weed optimization (IWO algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs with the criteria to minimize the maximum completion time (makespan, the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

  8. Global Optimization of Damping Ring Designs Using a Multi-Objective Evolutionary Algorithm

    CERN Document Server

    Emery, Louis

    2005-01-01

    Several damping ring designs for the International Linear Collider have been proposed recently. Some of the specifications, such as circumference and bunch train, are not fixed yet. Designers must make a choice anyway, select a geometry type (dog-bone or circular), an arc cell type (TME or FODO), and optimize linear and nonlinear part of the optics. The design process include straightforward steps (usually the linear optics), and some steps not so straightforward (when nonlinear optics optimization is affected by the linear optics). A first attempt at automating this process for the linear optics is reported. We first recognize that the optics is defined by just a few primary parameters (e.g., phase advance per cell) that determine the rest (e.g., quadrupole strength). In addition to the exact specification of circumference, equilibrium emittance and damping time there are some other quantities which could be optimized that may conflict with each other. A multiobjective genetic optimizer solves this problem b...

  9. 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.

  10. Multi-Objective Optimization of Spacecraft Trajectories for Small-Body Coverage Missions

    Science.gov (United States)

    Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren

    2017-01-01

    Visual coverage of surface elements of a small-body object requires multiple images to be taken that meet many requirements on their viewing angles, illumination angles, times of day, and combinations thereof. Designing trajectories capable of maximizing total possible coverage may not be useful since the image target sequence and the feasibility of said sequence given the rotation-rate limitations of the spacecraft are not taken into account. This work presents a means of optimizing, in a multi-objective manner, surface target sequences that account for such limitations.

  11. 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

    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......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...

  12. Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored

    Institute of Scientific and Technical Information of China (English)

    Carlos A. COELLO COELLO

    2009-01-01

    This paper provides a short review of some of the main topics in which the current research in evolutionary multi-objective optimization is being focused. The topics discussed include new algorithms, efficiency, relaxed forms of dominance, scalability, and alternative metaheuristics. This discussion motivates some further topics which,from the author's perspective, constitute good potential areas for future research, namely, constraint-handling techniques,incorporation of user's preferences and parameter control,This information is expected to be useful for those interested in pursuing research in this area.

  13. Multi-objective optimization of cellular scanning strategy in selective laser melting

    DEFF Research Database (Denmark)

    Ahrari, Ali; Deb, Kalyanmoy; Mohanty, Sankhya

    2017-01-01

    The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study...... is to develop a multi-objective approach to optimize the cellular scanning strategy such that the two aforementioned defects are minimized. The decision variable in the chosen problem is a combination of the sequence in which cells are processed and one of six scanning strategies applied to each cell. Thus...

  14. Optimality conditions and duality for a class of nondifferentiable multiobjective generalized fractional programming problems

    Institute of Scientific and Technical Information of China (English)

    GAO Ying; RONG Wei-dong

    2008-01-01

    This paper studies a class of multiobjective generalized fractional programming problems, where the numerators of objective functions are the sum of differentiable function and convex function, while the denominators are the difference of differentiable function and convex function. Under the assumption of Calmness Constraint Qualification the Kuhn-Tucker type necessary conditions for efficient solution are given, and the Kuhn-Tucker type sufficient conditions for efficient solution are presented under the assumptions of (F, α, ρ, d)-V-convexity.Subsequently, the optimality conditions for two kinds of duality models are formulated and duality theorems are proved.

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

    Directory of Open Access Journals (Sweden)

    Amr A. Adly

    2015-05-01

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

  16. Multiobjective Design Optimization of Supersonic Jet Engine in Different Cruise Mach Numbers

    Science.gov (United States)

    Ogawa, Masamichi; Sato, Tetsuya; Kobayashi, Hiroaki; Taguchi, Hideyuki

    The aim of this paper is to apply a multi-objective optimization generic algorithm (MOGA) to the conceptual design of the hypersonic/supersonic vehicles with different cruise Mach number. The pre-cooled turbojet engine is employed as a propulsion system and some engine parameters such as the precooler size, compressor size, compression ratio and fuel type are varied in the analysis. The result shows that the optimum cruise Mach number is about 4 if hydrogen fuel is used. Methane fuel instead of hydrogen reduces the vehicle gross weight by 33% in case of the Mach 2 vehicle.

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

    Science.gov (United States)

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

    2015-05-01

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

  18. Large-Scale Multi-Objective Optimization for the Management of Seawater Intrusion, Santa Barbara, CA

    Science.gov (United States)

    Stanko, Z. P.; Nishikawa, T.; Paulinski, S. R.

    2015-12-01

    The City of Santa Barbara, located in coastal southern California, is concerned that excessive groundwater pumping will lead to chloride (Cl) contamination of its groundwater system from seawater intrusion (SWI). In addition, the city wishes to estimate the effect of continued pumping on the groundwater basin under a variety of initial and climatic conditions. A SEAWAT-based groundwater-flow and solute-transport model of the Santa Barbara groundwater basin was optimized to produce optimal pumping schedules assuming 5 different scenarios. Borg, a multi-objective genetic algorithm, was coupled with the SEAWAT model to identify optimal management strategies. The optimization problems were formulated as multi-objective so that the tradeoffs between maximizing pumping, minimizing SWI, and minimizing drawdowns can be examined by the city. Decisions can then be made on a pumping schedule in light of current preferences and climatic conditions. Borg was used to produce Pareto optimal results for all 5 scenarios, which vary in their initial conditions (high water levels, low water levels, or current basin state), simulated climate (normal or drought conditions), and problem formulation (objective equations and decision-variable aggregation). Results show mostly well-defined Pareto surfaces with a few singularities. Furthermore, the results identify the precise pumping schedule per well that was suitable given the desired restriction on drawdown and Cl concentrations. A system of decision-making is then possible based on various observations of the basin's hydrologic states and climatic trends without having to run any further optimizations. In addition, an assessment of selected Pareto-optimal solutions was analyzed with sensitivity information using the simulation model alone. A wide range of possible groundwater pumping scenarios is available and depends heavily on the future climate scenarios and the Pareto-optimal solution selected while managing the pumping wells.

  19. 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.

  20. Multi-objective optimization of traf﬿c externalities using tolls: A comparison of genetic algorithm and game theoretical approach

    NARCIS (Netherlands)

    Ohazulike, Anthony; Brands, T.

    2013-01-01

    Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally

  1. Multi-objective Optimization of Traffic Externalities using Tolls: A Comparison of Genetic Algorithm with Game Theoretical Approach.

    NARCIS (Netherlands)

    Ohazulike, Anthony; Brands, Ties

    2013-01-01

    Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally

  2. Multi-objective Optimization of Traffic Externalities using Tolls: A Comparison of Genetic Algorithm with Game Theoretical Approach.

    NARCIS (Netherlands)

    Ohazulike, A.E.; Brands, T.

    2013-01-01

    Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally dis

  3. Optimization of Hierarchically Scheduled Heterogeneous Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Traian; Pop, Paul; Eles, Petru;

    2005-01-01

    We present an approach to the analysis and optimization of heterogeneous distributed embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource...

  4. Predictive multiobjective genetic algorithm for dynamic multiobjective optimization problems%动态多目标优化的预测遗传算法

    Institute of Scientific and Technical Information of China (English)

    武燕; 刘小雄; 池程芝

    2013-01-01

    Dynamic multiobjective optimization problems require an algorithm to continuously track a changing Pareto optimal solutions over time. Therefore, a new predictive multiobjective genetic algorithm(PMGA) is proposed, in which the centroid of Pareto optimal is soluted by clustering. And Pareto optimal solutions are described by applying the centroid points and reference solutions. Then the prediction set is generated by using the inertia predict and Gauss mutation. After an environment changed, the prediction set is incorporated in the current population to increase the population diversity by guided fashion. Finally, experimental studies on dynamic multiobjective optimization problems are carried out. The simulation results show that PMGA can quickly adapt the dynamic environments and track Pareto optimal solutions.%  为了在动态环境中很好地跟踪最优解,考虑动态优化问题的特点,提出一种新的多目标预测遗传算法。首先对 Pareto 前沿面进行聚类以求得解集的质心;其次应用该质心与参考点描述 Pareto 前沿面;再次通过预测方法给出预测点集,使得算法在环境变化后能够有指导地增加种群多样性,以便快速跟踪最优解;最后应用标准动态测试问题进行算法测试,仿真分析结果表明所提出算法能适应动态环境,快速跟踪 Pareto 前沿面。

  5. 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.

  6. Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

    Science.gov (United States)

    Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

    2014-01-01

    A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

  7. A multiobjective optimization approach to the operation and investment of the national energy and transportation systems

    Science.gov (United States)

    Ibanez, Eduardo

    Most U.S. energy usage is for electricity production and vehicle transportation, two interdependent infrastructures. The strength and number of the interdependencies will increase rapidly as hybrid electric transportation systems, including plug-in hybrid electric vehicles and hybrid electric trains, become more prominent. There are several new energy supply technologies reaching maturity, accelerated by public concern over global warming. The National Energy and Transportation Planning Tool (NETPLAN) is the implementation of the long-term investment and operation model for the transportation and energy networks. An evolutionary approach with underlying fast linear optimization are in place to determine the solutions with the best investment portfolios in terms of cost, resiliency and sustainability, i.e., the solutions that form the Pareto front. The popular NSGA-II algorithm is used as the base for the multiobjective optimization and metrics are developed for to evaluate the energy and transportation portfolios. An integrating approach to resiliency is presented, allowing the evaluation of high-consequence events, like hurricanes or widespread blackouts. A scheme to parallelize the multiobjective solver is presented, along with a decomposition method for the cost minimization program. The modular and data-driven design of the software is presented. The modeling tool is applied in a numerical example to optimize the national investment in energy and transportation in the next 40 years.

  8. Calculating complete and exact Pareto front for multiobjective optimization: a new deterministic approach for discrete problems.

    Science.gov (United States)

    Hu, Xiao-Bing; Wang, Ming; Di Paolo, Ezequiel

    2013-06-01

    Searching the Pareto front for multiobjective optimization problems usually involves the use of a population-based search algorithm or of a deterministic method with a set of different single aggregate objective functions. The results are, in fact, only approximations of the real Pareto front. In this paper, we propose a new deterministic approach capable of fully determining the real Pareto front for those discrete problems for which it is possible to construct optimization algorithms to find the k best solutions to each of the single-objective problems. To this end, two theoretical conditions are given to guarantee the finding of the actual Pareto front rather than its approximation. Then, a general methodology for designing a deterministic search procedure is proposed. A case study is conducted, where by following the general methodology, a ripple-spreading algorithm is designed to calculate the complete exact Pareto front for multiobjective route optimization. When compared with traditional Pareto front search methods, the obvious advantage of the proposed approach is its unique capability of finding the complete Pareto front. This is illustrated by the simulation results in terms of both solution quality and computational efficiency.

  9. Multi-Objective Random Search Algorithm for Simultaneously Optimizing Wind Farm Layout and Number of Turbines

    Science.gov (United States)

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

    2016-09-01

    A new algorithm for multi-objective wind farm layout optimization is presented. It formulates the wind turbine locations as continuous variables and is capable of optimizing the number of turbines and their locations in the wind farm simultaneously. Two objectives are considered. One is to maximize the total power production, which is calculated by considering the wake effects using the Jensen wake model combined with the local wind distribution. The other is to minimize the total electrical cable length. This length is assumed to be the total length of the minimal spanning tree that connects all turbines and is calculated by using Prim's algorithm. Constraints on wind farm boundary and wind turbine proximity are also considered. An ideal test case shows the proposed algorithm largely outperforms a famous multi-objective genetic algorithm (NSGA-II). In the real test case based on the Horn Rev 1 wind farm, the algorithm also obtains useful Pareto frontiers and provides a wide range of Pareto optimal layouts with different numbers of turbines for a real-life wind farm developer.

  10. 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.

  11. Multiobjective Optimal Reactive Power Dispatch Considering Voltage Stability Using Shuffled Frog Leaping Algorithm

    Directory of Open Access Journals (Sweden)

    V. Tamilselvan

    2015-05-01

    Full Text Available This study addresses a shuffled frog leaping algorithm for solving the multi-objective reactive power dispatch problem in a power system. Optimal Reactive Power Dispatch (ORPD is formulated as a nonlinear, multi-modal and mixed-variable problem. The intended technique is based on the minimization of the real power loss, minimization of voltage deviation and maximization of the voltage stability margin. Generator voltages, capacitor banks and tap positions of tap changing transformers are used as optimization variables of this problem. A memetic meta-heuristic named as shuffled frog-leaping algorithm is intended to solve multi-objective optimal reactive power dispatch problems considering voltage stability margin and voltage deviation. The Shuffled Frog-Leaping Algorithm (SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The most important benefit of this algorithm is higher speed of convergence to a better solution. The intended method is applied to ORPD problem on IEEE 57 bus power systems and compared with two versions of differential evolutionary algorithm. The simulation results show the effectiveness of the intended method.

  12. Applying multi-objective genetic algorithms in green building design optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Weimin; Zmeureanu, Radu [Department of Building, Civil and Environmental Engineering, Centre for Building Studies, Concordia University, Montreal (Canada); Rivard, Hugues [Department of Construction Engineering, Ecole de Technologie Superieure, Montreal (Canada)

    2005-11-01

    Since buildings have considerable impacts on the environment, it has become necessary to pay more attention to environmental performance in building design. However, it is a difficult task to find better design alternatives satisfying several conflicting criteria, especially, economical and environmental performance. This paper presents a multi-objective optimization model that could assist designers in green building design. Variables in the model include those parameters that are usually determined at the conceptual design stage and that have critical influence on building performance. Life cycle analysis methodology is employed to evaluate design alternatives for both economical and environmental criteria. Life cycle environmental impacts are evaluated in terms of expanded cumulative exergy consumption, which is the sum of exergy consumption due to resource inputs and abatement exergy required to recover the negative impacts due to waste emissions. A multi-objective genetic algorithm is employed to find optimal solutions. A case study is presented and the effectiveness of the approach is demonstrated for identifying a number of Pareto optimal solutions for green building design. (author)

  13. Multiobjective adaptive surrogate modeling-based optimization for parameter estimation of large, complex geophysical models

    Science.gov (United States)

    Gong, Wei; Duan, Qingyun; Li, Jianduo; Wang, Chen; Di, Zhenhua; Ye, Aizhong; Miao, Chiyuan; Dai, Yongjiu

    2016-03-01

    Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multiobjective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes toward the region that can improve all of the objectives simultaneously. We tested the MO-ASMO algorithm against NSGA-II and SUMO with 13 test functions and a land surface model - the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO-ASMO.

  14. Time Intervals for Maintenance of Offshore Structures Based on Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Dante Tolentino

    2013-01-01

    Full Text Available With the aim of establishing adequate time intervals for maintenance of offshore structures, an approach based on multiobjective optimization for making decisions is proposed. The formulation takes into account the degradation of the mechanical properties of the structures and its influence over time on both the structural capacity and the structural demand, given a maximum wave height. The set of time intervals for maintenance corresponds to a balance between three objectives: (a structural reliability, (b damage index, and (c expected cumulative total cost. Structural reliability is expressed in terms of confidence factors as functions of time by means of closed-form mathematical expressions which consider structural deterioration. The multiobjective optimization is solved using an evolutionary genetic algorithm. The approach is applied to an offshore platform located at Campeche Bay in the Gulf of Mexico. The optimization criterion includes the reconstruction of the platform. Results indicate that if the first maintenance action is made in 5 years after installing the structure, the second repair action should be made in the following 7 to 10 years; however, if the first maintenance action is made in 6 years after installing the structure, then the second should be made in the following 5 to 8 years.

  15. 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.

  16. Optimization of Exposure Time Division for Multi-object Photometry

    Science.gov (United States)

    Popowicz, Adam; Kurek, Aleksander R.

    2017-09-01

    Optical observations of wide fields of view entail the problem of selecting the best exposure time. As many objects are usually observed simultaneously, the quality of photometry of the brightest ones is always better than that of the dimmer ones, even though all of them are frequently equally interesting for astronomers. Thus, measuring all objects with the highest possible precision is desirable. In this paper, we present a new optimization algorithm, dedicated for the division of exposure time into sub-exposures, which enables photometry with a more balanced noise budget. The proposed technique increases the photometric precision of dimmer objects at the expense of the measurement fidelity of the brightest ones. We have tested the method on real observations using two telescope setups, demonstrating its usefulness and good consistency with theoretical expectations. The main application of our approach is a wide range of sky surveys, including ones performed by space telescopes. The method can be used to plan virtually any photometric observation of objects that show a wide range of magnitudes.

  17. Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

    Science.gov (United States)

    Rani, R Ranjani; Ramyachitra, D

    2016-12-01

    Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods.

  18. Multiobjective optimization for Groundwater Nitrate Pollution Control. Application to El Salobral-Los Llanos aquifer (Spain).

    Science.gov (United States)

    Llopis-Albert, C.; Peña-Haro, S.; Pulido-Velazquez, M.; Molina, J.

    2012-04-01

    Water quality management is complex due to the inter-relations between socio-political, environmental and economic constraints and objectives. In order to choose an appropriate policy to reduce nitrate pollution in groundwater it is necessary to consider different objectives, often in conflict. In this paper, a hydro-economic modeling framework, based on a non-linear optimization(CONOPT) technique, which embeds simulation of groundwater mass transport through concentration response matrices, is used to study optimal policies for groundwater nitrate pollution control under different objectives and constraints. Three objectives were considered: recovery time (for meeting the environmental standards, as required by the EU Water Framework Directive and Groundwater Directive), maximum nitrate concentration in groundwater, and net benefits in agriculture. Another criterion was added: the reliability of meeting the nitrate concentration standards. The approach allows deriving the trade-offs between the reliability of meeting the standard, the net benefits from agricultural production and the recovery time. Two different policies were considered: spatially distributed fertilizer standards or quotas (obtained through multi-objective optimization) and fertilizer prices. The multi-objective analysis allows to compare the achievement of the different policies, Pareto fronts (or efficiency frontiers) and tradeoffs for the set of mutually conflicting objectives. The constraint method is applied to generate the set of non-dominated solutions. The multi-objective framework can be used to design groundwater management policies taking into consideration different stakeholders' interests (e.g., policy makers, agricultures or environmental groups). The methodology was applied to the El Salobral-Los Llanos aquifer in Spain. Over the past 30 years the area has undertaken a significant socioeconomic development, mainly due to the intensive groundwater use for irrigated crops, which has

  19. A multiobjective discrete stochastic optimization approach to shared aquifer management: Methodology and application

    Science.gov (United States)

    Siegfried, Tobias; Kinzelbach, Wolfgang

    2006-02-01

    Negative effects from groundwater mining are observed globally. They threaten future supply locally. Especially in semiarid to arid regions, where aquifers are the sole freshwater resource, this is problematic and can lead to an excessive rise of provision costs. Proper resource management in such environments is crucial. In many instances, however, aquifers are common property resources. In such cases and depending on resource characteristics and the nature of competing uses, their management is inherently multiobjective, and benefits from cooperative management are likely to be substantial. This paper presents a methodology for the determination of optimal, cooperative allocation policies in multiobjective aquifer management problems. Our model couples a finite difference aquifer model with an economic model that accounts for water provision costs. Discounted temporal installation and pumping and conveyance costs determine the vector-valued objective function. Each of the objectives characterizes the individual present costs over a given time horizon that the corresponding decision makers wish to minimize. Constraint handling is implemented by the option of moving wells. A multiobjective evolutionary algorithm is coupled to the management model so as to approximate cooperative tradeoff policies on the Pareto surface. These solutions can be ranked against existing, noncooperative status quo strategies. Consequently, the simulation-optimization model is applied to the northwest Sahara aquifer system which is used noncooperatively as a resource by Algeria, Tunisia, and Libya. We find that significant capital gains can be achieved by the establishment of intelligent pump scheduling. Since each country could benefit, a strong incentive toward the implementation of such cooperative strategies exists.

  20. A spatial multi-objective optimization model for sustainable urban wastewater system layout planning.

    Science.gov (United States)

    Dong, X; Zeng, S; Chen, J

    2012-01-01

    Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

  1. Multi-object optimization design for differential and grading toothed roll crusher using a genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHAO La-la; WANG Zhong-bin; ZANG Feng

    2008-01-01

    Our differential and grading toothed roll crusher blends the advantages of a toothed roll crusher and a jaw crusher and possesses characteristics of great crushing, high breaking efficiency, multi-sieving and has, for the moment, made up for the shortcomings of the toothed roll crusher. The moving jaw of the crusher is a crank-rocker mechanism. For optimizing the dynamic performance and improving the cracking capability of the crusher, a mathematical model was established to optimize the transmission angle γ and to minimize the travel characteristic value m of the moving jaw. Genetic algorithm is used to optimize the crusher crank-rocker mechanism for multi-object design and an optimum result is obtained. According to the implementation, it is shown that the performance of the crusher and the cracking capability of the moving jaw have been improved.

  2. Using multi-objective optimization to design parameters in electro-discharge machining by wire

    Directory of Open Access Journals (Sweden)

    Carlos Alberto OCHOA

    2015-03-01

    Full Text Available The following paper describes the main objective to follow the methodology used and proposed to obtain the optimal values of WEDM process operation on the machine Robofil 310 by robust parameter design (RPD of Dr. G. Taguichi [TAGUCHI, G. 1993], through controllable factors which result in more inferences regarding the problem to noise signal (S / N, which for this study is the variability of the hardness of samples from 6061, also studied the behaviour of the output parameters as the material removal rate (MRR and surface roughness (Ra, subsequently took the RPD orthogonal array and characterized the individuals in the population, each optimal value is a gene and each possible solution is a chromosome, used multi-objective optimization using Non-dominated Sorting Genetic Algorithm to cross and mutate this population to generate better results MRR and Ra.

  3. Multi-objective Optimization of Controller for Process with Reverse Response and Dead Time

    Institute of Scientific and Technical Information of China (English)

    WANG Guo-liang; SHAO Hui-he

    2009-01-01

    Due to the difficulty of controlling the process with inverse response and dead time, a Multi-objective Optimization based on Genetic Algorithm (MOGA) method for tuning of proportional-integral-derivative (PID) controller is proposed. The settings of the controller are valued by two criteria, the error between output and reference signals and control moves. An appropriate set of Pareto optimal setting of the PID controller is founded by analyzing the results of Pareto optimal surfaces for balancing the two criteria. A high order process with inverse response and dead time is used to illustrate the results of the proposed method. And the efficiency and robustness of the tuning method are evident compared with methods in recent literature.

  4. Exact algorithms for OWA-optimization in multiobjective spanning tree problems

    CERN Document Server

    Galand, Lucie

    2009-01-01

    This paper deals with the multiobjective version of the optimal spanning tree problem. More precisely, we are interested in determining the optimal spanning tree according to an Ordered Weighted Average (OWA) of its objective values. We first show that the problem is weakly NP-hard. In the case where the weights of the OWA are strictly decreasing, we then propose a mixed integer programming formulation, and provide dedicated optimality conditions yielding an important reduction of the size of the program. Next, we present two bounds that can be used to prune subspaces of solutions either in a shaving phase or in a branch and bound procedure. The validity of these bounds does not depend on specific properties of the weights (apart from non-negativity). All these exact resolution algorithms are compared on the basis of numerical experiments, according to their respective validity scopes.

  5. Dual-mode nested search method for categorical uncertain multi-objective optimization

    Science.gov (United States)

    Tang, Long; Wang, Hu

    2016-10-01

    Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

  6. Multi-objective optimization of the management of a waterworks using an integrated well field model

    DEFF Research Database (Denmark)

    Hansen, Annette Kirstine; Bauer-Gottwein, Peter; Rosbjerg, Dan

    2012-01-01

    This study uses multi-objective optimization of an integrated well field model to improve the management of a waterworks. The well field model, called WELLNES (WELL field Numerical Engine Shell) is a dynamic coupling of a groundwater model, a pipe network model, and a well model. WELLNES is capable...... of predicting the water level and the energy consumption of the individual production wells. The model has been applied to Søndersø waterworks in Denmark, where it predicts the energy consumption within 1.8% of the observed. The objectives of the optimization problem are to minimize the specific energy...... provides the decision-makers with compromise solutions between the two competing objectives. In the test case the Pareto optimal solutions are compared with an exhaustive benchmark solution. It is shown that the energy consumption can be reduced by 4% by changing the pumping configuration without violating...

  7. Aircraft concept optimization using the global sensitivity approach and parametric multiobjective figures of merit

    Science.gov (United States)

    Malone, Brett; Mason, W. H.

    1992-01-01

    An extension of our parametric multidisciplinary optimization method to include design results connecting multiple objective functions is presented. New insight into the effect of the figure of merit (objective function) on aircraft configuration size and shape is demonstrated using this technique. An aircraft concept, subject to performance and aerodynamic constraints, is optimized using the global sensitivity equation method for a wide range of objective functions. These figures of merit are described parametrically such that a series of multiobjective optimal solutions can be obtained. Computational speed is facilitated by use of algebraic representations of the system technologies. Using this method, the evolution of an optimum design from one objective function to another is demonstrated. Specifically, combinations of minimum takeoff gross weight, fuel weight, and maximum cruise performance and productivity parameters are used as objective functions.

  8. A New Evolutionary Algorithm for Solving Multi-Objective Optimization Problems

    Institute of Scientific and Technical Information of China (English)

    Chen Wen-ping; Kang Li-shan

    2003-01-01

    Multi-objective optimization is a new focus of evolutionary computation research. This paper puts forward a new algorithm, which can not only converge quickly, but also keep diversity among population efficiently, in order to find the Pareto-optimal set. This new algorithm replaces the worst individual with a newly-created one by "multi parent crossover", so that the population could converge near the true Pareto-optimal solutions in the end. At the same time, this new algorithm adopts niching and fitness-sharing techniques to keep the population in a good distribution. Numerical experiments show that the algorithm is rather effective in solving some Benchmarks. No matter whether the Pareto front of problems is convex or non-convex, continuous or discontinuous, and the problems are with constraints or not, the program turns out to do well.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ahmadi, Pouria; Rosen, Marc A.; Dincer, Ibrahim [Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (Canada)], email: Pouria.Ahmadi@uoit.ca, email: Marc.Rosen@uoit.ca, email: Ibrahim.Dincer@uoit.ca

    2011-07-01

    World-wide, many applications are significant users of energy and with the depletion of fossil fuels and climate change, it is important that energy be used more efficiently and sustainably. Exergy analysis uses the second law of thermodynamics to identify and understand sustainable energy options. The aim of this paper is to present the thermodynamic modeling and optimization of a polygeneration energy system. A multi-objective optimization method was used to find the best design parameters of the polygeneration energy system and 2 objective functions were used to minimize the total cost rate and maximize the system exergy efficiency. In addition, the impacts of different parameters on the exergy efficiency and CO2 emission were studied. This study provided a better understanding of the performance of polygeneration energy systems and provided a closed form equation to help designers in optimizing polygeneration plants.

  10. Aircraft concept optimization using the global sensitivity approach and parametric multiobjective figures of merit

    Science.gov (United States)

    Malone, Brett; Mason, W. H.

    1992-01-01

    An extension of our parametric multidisciplinary optimization method to include design results connecting multiple objective functions is presented. New insight into the effect of the figure of merit (objective function) on aircraft configuration size and shape is demonstrated using this technique. An aircraft concept, subject to performance and aerodynamic constraints, is optimized using the global sensitivity equation method for a wide range of objective functions. These figures of merit are described parametrically such that a series of multiobjective optimal solutions can be obtained. Computational speed is facilitated by use of algebraic representations of the system technologies. Using this method, the evolution of an optimum design from one objective function to another is demonstrated. Specifically, combinations of minimum takeoff gross weight, fuel weight, and maximum cruise performance and productivity parameters are used as objective functions.

  11. Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

    Directory of Open Access Journals (Sweden)

    M. Gorji-Bandpy

    2012-01-01

    Full Text Available A new method called mutable smart bee (MSB algorithm proposed for cooperative optimizing of the maximum power output (MPO and minimum entropy generation (MEG of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA, improved particle swarm optimization (IPSO, Lukasik firefly algorithm (LFFA, and self-adaptive penalty function genetic algorithm (SAPF-GA. According to obtained results, it can be concluded that Mutable Smart Bee (MSB is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

  12. Optimization of Hierarchical Modulation for Use of Scalable Media

    Directory of Open Access Journals (Sweden)

    Heneghan Conor

    2010-01-01

    Full Text Available This paper studies the Hierarchical Modulation, a transmission strategy of the approaching scalable multimedia over frequency-selective fading channel for improving the perceptible quality. An optimization strategy for Hierarchical Modulation and convolutional encoding, which can achieve the target bit error rates with minimum global signal-to-noise ratio in a single-user scenario, is suggested. This strategy allows applications to make a free choice of relationship between Higher Priority (HP and Lower Priority (LP stream delivery. The similar optimization can be used in multiuser scenario. An image transport task and a transport task of an H.264/MPEG4 AVC video embedding both QVGA and VGA resolutions are simulated as the implementation example of this optimization strategy, and demonstrate savings in SNR and improvement in Peak Signal-to-Noise Ratio (PSNR for the particular examples shown.

  13. Multi-objective optimization of the control strategy of electric vehicle electro-hydraulic composite braking system with genetic algorithm

    OpenAIRE

    Zhang Fengjiao; Wei Minxiang

    2015-01-01

    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 el...

  14. Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm

    Directory of Open Access Journals (Sweden)

    John Anish K.

    2017-01-01

    Full Text Available Matrix Heat Exchanger is having wide spread applications in cryogenics and aerospace, where high effectiveness and compactness is essential. This can be achieved by providing high thermal conductive plates and low thermal conductive spacers alternately. These perforated plate matrix heat exchangers have near to 100% efficiency due to low longitudinal heat transfer. The heat transfer and flow friction characteristics of a perforated plate matrix heat exchanger can be represented using Colburn factor and friction factor. In this paper, dimensionless parameters like Reynolds number (Re, porosity (p, perforation perimeter factor (P f, plate thickness to pore diameter ratio (l/d and spacer thickness to plate thickness ratio (s/l have been optimized for maximum Colburn factor and minimum friction factor using genetic algorithm. Two algorithms, one for single objective and the other for multi-objective problems, which are believed to be more efficient, are described. The algorithms coded with MATLAB, is used to perform multi-objective optimization on perforated plate matrix heat exchanger surfaces. The results show promising results.

  15. Parameter Estimation for Coupled Hydromechanical Simulation of Dynamic Compaction Based on Pareto Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Wei Wang

    2015-01-01

    Full Text Available This paper presented a parameter estimation method based on a coupled hydromechanical model of dynamic compaction and the Pareto multiobjective optimization technique. The hydromechanical model of dynamic compaction is established in the FEM program LS-DYNA. The multiobjective optimization algorithm, Nondominated Sorted Genetic Algorithm (NSGA-IIa, is integrated with the numerical model to identify soil parameters using multiple sources of field data. A field case study is used to demonstrate the capability of the proposed method. The observed pore water pressure and crater depth at early blow of dynamic compaction are simultaneously used to estimate the soil parameters. Robustness of the back estimated parameters is further illustrated by a forward prediction. Results show that the back-analyzed soil parameters can reasonably predict lateral displacements and give generally acceptable predictions of dynamic compaction for an adjacent location. In addition, for prediction of ground response of the dynamic compaction at continuous blows, the prediction based on the second blow is more accurate than the first blow due to the occurrence of the hardening and strengthening of soil during continuous compaction.

  16. Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

    Science.gov (United States)

    Wu, Kai; Liu, Jing; Wang, Shuai

    2016-11-01

    Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy.

  17. Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

    Institute of Scientific and Technical Information of China (English)

    Jie Zhang

    2006-01-01

    In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

  18. Surrogate-based Multi-Objective Optimization and Uncertainty Quantification Methods for Large, Complex Geophysical Models

    Science.gov (United States)

    Gong, Wei; Duan, Qingyun

    2016-04-01

    Parameterization scheme has significant influence to the simulation ability of large, complex dynamic geophysical models, such as distributed hydrological models, land surface models, weather and climate models, etc. with the growing knowledge of physical processes, the dynamic geophysical models include more and more processes and producing more output variables. Consequently the parameter optimization / uncertainty quantification algorithms should also be multi-objective compatible. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this research, we have developed surrogate-based multi-objective optimization method (MO-ASMO) and Markov Chain Monte Carlo method (MC-ASMO) for uncertainty quantification for these expensive dynamic models. The aim of MO-ASMO and MC-ASMO is to reduce the total number of model runs with appropriate adaptive sampling strategy assisted by surrogate modeling. Moreover, we also developed a method that can steer the search process with the help of prior parameterization scheme derived from the physical processes involved, so that all of the objectives can be improved simultaneously. The proposed algorithms have been evaluated with test problems and a land surface model - the Common Land Model (CoLM). The results demonstrated their effectiveness and efficiency.

  19. Multiobjective Optimization of Turning Cutting Parameters for J-Steel Material

    Directory of Open Access Journals (Sweden)

    Adel T. Abbas

    2016-01-01

    Full Text Available This paper presents a multiobjective optimization study of cutting parameters in turning operation for a heat-treated alloy steel material (J-Steel with Vickers hardness in the range of HV 365–395 using uncoated, unlubricated Tungsten-Carbide tools. The primary aim is to identify proper settings of the cutting parameters (cutting speed, feed rate, and depth of cut that lead to reasonable compromises between good surface quality and high material removal rate. Thorough exploration of the range of cutting parameters was conducted via a five-level full-factorial experimental matrix of samples and the Pareto trade-off frontier is identified. The trade-off among the objectives was observed to have a “knee” shape, in which certain settings for the cutting parameters can achieve both good surface quality and high material removal rate within certain limits. However, improving one of the objectives beyond these limits can only happen at the expense of a large compromise in the other objective. An alternative approach for identifying the trade-off frontier was also tested via multiobjective implementation of the Efficient Global Optimization (m-EGO algorithm. The m-EGO algorithm was successful in identifying two points within the good range of the trade-off frontier with 36% fewer experimental samples.

  20. Long Series Multi-objectives Optimal Operation of Water And Sediment Regulation

    Science.gov (United States)

    Bai, T.; Jin, W.

    2015-12-01

    Secondary suspended river in Inner Mongolia reaches have formed and the security of reach and ecological health of the river are threatened. Therefore, researches on water-sediment regulation by cascade reservoirs are urgent and necessary. Under this emergency background, multi-objectives water and sediment regulation are studied in this paper. Firstly, multi-objective optimal operation models of Longyangxia and Liujiaxia cascade reservoirs are established. Secondly, based on constraints handling and feasible search space techniques, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is greatly improved to solve the model. Thirdly, four different scenarios are set. It is demonstrated that: (1) scatter diagrams of perato front are obtained to show optimal solutions of power generation maximization, sediment maximization and the global equilibrium solutions between the two; (2) the potentiality of water-sediment regulation by Longyangxia and Liujiaxia cascade reservoirs are analyzed; (3) with the increasing water supply in future, conflict between water supply and water-sediment regulation occurred, and the sustainability of water and sediment regulation will confront with negative influences for decreasing transferable water in cascade reservoirs; (4) the transfer project has less benefit for water-sediment regulation. The research results have an important practical significance and application on water-sediment regulation by cascade reservoirs in the Upper Yellow River, to construct water and sediment control system in the whole Yellow River basin.

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

    Directory of Open Access Journals (Sweden)

    Xuanhu He

    2015-03-01

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

  2. Development of a pump-turbine runner based on multiobjective optimization

    Science.gov (United States)

    Xuhe, W.; Baoshan, Z.; Lei, T.; Jie, Z.; Shuliang, C.

    2014-03-01

    As a key component of reversible pump-turbine unit, pump-turbine runner rotates at pump or turbine direction according to the demand of power grid, so higher efficiencies under both operating modes have great importance for energy saving. In the present paper, a multiobjective optimization design strategy, which includes 3D inverse design method, CFD calculations, response surface method (RSM) and multiobjective genetic algorithm (MOGA), is introduced to develop a model pump-turbine runner for middle-high head pumped storage plant. Parameters that controlling blade shape, such as blade loading and blade lean angle at high pressure side are chosen as input parameters, while runner efficiencies under both pump and turbine modes are selected as objective functions. In order to validate the availability of the optimization design system, one runner configuration from Pareto front is manufactured for experimental research. Test results show that the highest unit efficiency is 91.0% under turbine mode and 90.8% under pump mode for the designed runner, of which prototype efficiencies are 93.88% and 93.27% respectively. Viscous CFD calculations for full passage model are also conducted, which aim at finding out the hydraulic improvement from internal flow analyses.

  3. Autonomous robot navigation based on the evolutionary multi-objective optimization of potential fields

    Science.gov (United States)

    Herrera Ortiz, Juan Arturo; Rodríguez-Vázquez, Katya; Padilla Castañeda, Miguel A.; Arámbula Cosío, Fernando

    2013-01-01

    This article presents the application of a new multi-objective evolutionary algorithm called RankMOEA to determine the optimal parameters of an artificial potential field for autonomous navigation of a mobile robot. Autonomous robot navigation is posed as a multi-objective optimization problem with three objectives: minimization of the distance to the goal, maximization of the distance between the robot and the nearest obstacle, and maximization of the distance travelled on each field configuration. Two decision makers were implemented using objective reduction and discrimination in performance trade-off. The performance of RankMOEA is compared with NSGA-II and SPEA2, including both decision makers. Simulation experiments using three different obstacle configurations and 10 different routes were performed using the proposed methodology. RankMOEA clearly outperformed NSGA-II and SPEA2. The robustness of this approach was evaluated with the simulation of different sensor masks and sensor noise. The scheme reported was also combined with the wavefront-propagation algorithm for global path planning.

  4. Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

    Directory of Open Access Journals (Sweden)

    Mohd Arfian Ismail

    2017-09-01

    Full Text Available In this paper, an improve method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contain with many components. In addition, the multi-objective problem also need to be considered. Due to that, this study proposed and improve method that comprises with Newton method, differential evolution algorithm (DE and competitive co-evolutionary algorithm(ComCA. The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems and the experimental results show that the proposed method perform well compared to other works.

  5. Optimal atlas construction through hierarchical image registration

    Science.gov (United States)

    Grevera, George J.; Udupa, Jayaram K.; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Atlases (digital or otherwise) are common in medicine. However, there is no standard framework for creating them from medical images. One traditional approach is to pick a representative subject and then proceed to label structures/regions of interest in this image. Another is to create a "mean" or average subject. Atlases may also contain more than a single representative (e.g., the Visible Human contains both a male and a female data set). Other criteria besides gender may be used as well, and the atlas may contain many examples for a given criterion. In this work, we propose that atlases be created in an optimal manner using a well-established graph theoretic approach using a min spanning tree (or more generally, a collection of them). The resulting atlases may contain many examples for a given criterion. In fact, our framework allows for the addition of new subjects to the atlas to allow it to evolve over time. Furthermore, one can apply segmentation methods to the graph (e.g., graph-cut, fuzzy connectedness, or cluster analysis) which allow it to be separated into "sub-atlases" as it evolves. We demonstrate our method by applying it to 50 3D CT data sets of the chest region, and by comparing it to a number of traditional methods using measures such as Mean Squared Difference, Mattes Mutual Information, and Correlation, and for rigid registration. Our results demonstrate that optimal atlases can be constructed in this manner and outperform other methods of construction using freely available software.

  6. Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs.

    Science.gov (United States)

    Nicolotti, Orazio; Gillet, Valerie J; Fleming, Peter J; Green, Darren V S

    2002-11-07

    Deriving quantitative structure-activity relationship (QSAR) models that are accurate, reliable, and easily interpretable is a difficult task. In this study, two new methods have been developed that aim to find useful QSAR models that represent an appropriate balance between model accuracy and complexity. Both methods are based on genetic programming (GP). The first method, referred to as genetic QSAR (or GPQSAR), uses a penalty function to control model complexity. GPQSAR is designed to derive a single linear model that represents an appropriate balance between the variance and the number of descriptors selected for the model. The second method, referred to as multiobjective genetic QSAR (MoQSAR), is based on multiobjective GP and represents a new way of thinking of QSAR. Specifically, QSAR is considered as a multiobjective optimization problem that comprises a number of competitive objectives. Typical objectives include model fitting, the total number of terms, and the occurrence of nonlinear terms. MoQSAR results in a family of equivalent QSAR models where each QSAR represents a different tradeoff in the objectives. A practical consideration often overlooked in QSAR studies is the need for the model to promote an understanding of the biochemical response under investigation. To accomplish this, chemically intuitive descriptors are needed but do not always give rise to statistically robust models. This problem is addressed by the addition of a further objective, called chemical desirability, that aims to reward models that consist of descriptors that are easily interpretable by chemists. GPQSAR and MoQSAR have been tested on various data sets including the Selwood data set and two different solubility data sets. The study demonstrates that the MoQSAR method is able to find models that are at least as good as models derived using standard statistical approaches and also yields models that allow a medicinal chemist to trade statistical robustness for chemical

  7. Multi-objective robust airfoil optimization based on non-uniform rational B-spline (NURBS) representation

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    In order to improve airfoil performance under different flight conditions and to make the performance insensitive to off-design condition at the same time,a multi-objective optimization approach considering robust design has been developed and applied to airfoil design. Non-uniform rational B-spline (NURBS) representation is adopted in airfoil design process,control points and related weights around airfoil are used as design variables. Two airfoil representation cases show that the NURBS method can get airfoil geometry with max geometry error less than 0.0019. By using six-sigma robust approach in multi-objective airfoil design,each sub-objective function of the problem has robustness property. By adopting multi-objective genetic algorithm that is based on non-dominated sorting,a set of non-dominated airfoil solutions with robustness can be obtained in the design. The optimum robust airfoil can be traded off and selected in these non-dominated solutions by design tendency. By using the above methods,a multi-objective robust optimization was conducted for NASA SC0712 airfoil. After performing robust airfoil optimization,the mean value of drag coefficient at Ma0.7-0.8 and the mean value of lift coefficient at post stall regime (Ma0.3) have been improved by 12.2% and 25.4%. By comparing the aerodynamic force coefficients of optimization result,it shows that: different from single robust airfoil design which just improves the property of drag divergence at Ma0.7-0.8,multi-objective robust design can improve both the drag divergence property at Ma0.7-0.8 and stall property at low speed. The design cases show that the multi-objective robust design method makes the airfoil performance robust under different off-design conditions.

  8. Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy

    Energy Technology Data Exchange (ETDEWEB)

    Ruotsalainen, Henri [Department of Physics and Mathematics, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio (Finland); Miettinen, Kaisa [Department of Mathematical Information Technology, PO Box 35 (Agora), FI-40014 University of Jyvaeskylae (Finland); Palmgren, Jan-Erik; Lahtinen, Tapani, E-mail: henrimatias.ruotsalainen@gmail.co [Department of Oncology, Kuopio University Hospital, PO Box 1777, FI-70211 Kuopio (Finland)

    2010-08-21

    In this paper, we present an anatomy-based three-dimensional dose optimization approach for HDR brachytherapy using interactive multiobjective optimization (IMOO). In brachytherapy, the goals are to irradiate a tumor without causing damage to healthy tissue. These goals are often conflicting, i.e. when one target is optimized the other will suffer, and the solution is a compromise between them. IMOO is capable of handling multiple and strongly conflicting objectives in a convenient way. With the IMOO approach, a treatment planner's knowledge is used to direct the optimization process. Thus, the weaknesses of widely used optimization techniques (e.g. defining weights, computational burden and trial-and-error planning) can be avoided, planning times can be shortened and the number of solutions to be calculated is small. Further, plan quality can be improved by finding advantageous trade-offs between the solutions. In addition, our approach offers an easy way to navigate among the obtained Pareto optimal solutions (i.e. different treatment plans). When considering a simulation model of clinical 3D HDR brachytherapy, the number of variables is significantly smaller compared to IMRT, for example. Thus, when solving the model, the CPU time is relatively short. This makes it possible to exploit IMOO to solve a 3D HDR brachytherapy optimization problem. To demonstrate the advantages of IMOO, two clinical examples of optimizing a gynecologic cervix cancer treatment plan are presented.

  9. Probabilistic Assessment of TTC and Risk in Power Systems Using Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    javad kafi kondori

    2013-02-01

    Full Text Available Increasing demand for the amount and the value of electricity consumption in recent decades, communication, security and continuity to the electric grid has a great significance. Transmission networks as the main element in the power network has an important role in covering consumer’s needs. Various indices for evaluating the transmission network are defined and among them TTC is evaluated to determine the ability of the network in different economic conditions. In this paper, the probabilistic assessment of TTC is done and by solving a multi-objective optimization problem different values of TTC are obtained for different risks. Objectives that considered in this optimization are increasing TTC and reducing the risk. In probability assessment of TTC the uncertainty of generators and transmission lines are considered. To select contingencies, the probability of outage and the amount of TTC are considered. The IEEE reliability test system is used to demonstrate the effectiveness of the approach.

  10. Demonstrating the Applicability of PAINT to Computationally Expensive Real-life Multiobjective Optimization

    CERN Document Server

    Hartikainen, Markus

    2011-01-01

    We demonstrate the applicability of a new PAINT method to speed up iterations of interactive methods in multiobjective optimization. As our test case, we solve a computationally expensive non-linear, five-objective problem of designing and operating a wastewater treatment plant. The PAINT method interpolates between a given set of Pareto optimal outcomes and constructs a computationally inexpensive mixed integer linear surrogate problem for the original problem. We develop an IND-NIMBUS(R) PAINT module to combine the interactive NIMBUS method and the PAINT method and to find a preferred solution to the original problem. With the PAINT method, the solution process with the NIMBUS method take a comparatively short time even though the original problem is computationally expensive.

  11. Multi-objective fuzzy particle swarm optimization based on elite archiving and its convergence

    Institute of Scientific and Technical Information of China (English)

    Wei Jingxuan; Wang Yuping

    2008-01-01

    A fuzzy particle swarm optimization (PSO) on the basis of elite archiving is proposed for solving multi-objective optimization problems.First,a new perturbation operator is designed,and the concepts of fuzzy global best and fuzzy personal best are given on basis of the new operator.After that,particle updating equations are revised on the basis of the two new concepts to discourage the premature convergence and enlarge the potential search space; second,the elite archiving technique is used during the process of evolution,namely,the elite particles are introduced into the swarm,whereas the inferior particles are deleted.Therefore,the quality of the swarm is ensured.Finally,the convergence of this swarm is proved.The experimental results show that the nondominated solutions found by the proposed algorithm are uniformly distributed and widely spread along the Pareto front.

  12. Comparison of Planar Parallel Manipulator Architectures based on a Multi-objective Design Optimization Approach

    CERN Document Server

    Chablat, Damien; Ur-Rehman, Raza; Wenger, Philippe

    2010-01-01

    This paper deals with the comparison of planar parallel manipulator architectures based on a multi-objective design optimization approach. The manipulator architectures are compared with regard to their mass in motion and their regular workspace size, i.e., the objective functions. The optimization problem is subject to constraints on the manipulator dexterity and stiffness. For a given external wrench, the displacements of the moving platform have to be smaller than given values throughout the obtained maximum regular dexterous workspace. The contributions of the paper are highlighted with the study of 3-RPR, 3-RPR and 3-RPR planar parallel manipulator architectures, which are compared by means of their Pareto frontiers obtained with a genetic algorithm.

  13. Multi-objective Optimization of Large Wind Farm Parameters for Harmonic Instability and Resonance Conditions

    DEFF Research Database (Denmark)

    Ebrahimzadeh, Esmaeil; Blaabjerg, Frede; Wang, Xiongfei;

    2016-01-01

    In large wind farms, the mutual interactions between the power converter control systems and passive components may result in harmonic instability and resonance frequencies at a various frequency range. This paper presents an optimized parameter design of the power converter controllers in large...... wind farms in order to reduce the resonance probability and guarantee harmonic stability. In fact, a general multiobjective optimization procedure based on the genetic algorithm is proposed to set the poles of the wind farm in a desired location in order to minimize the number of the resonance...... frequencies and to improve the harmonic stability. Time-domain simulations of a 400-MW wind farm in the PSCAD/EMTDC environment demonstrate the effectiveness of the proposed design technique....

  14. Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2015-01-01

    Full Text Available A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

  15. Multi-objective optimization approach for cost management during product design at the conceptual phase

    Science.gov (United States)

    Durga Prasad, K. G.; Venkata Subbaiah, K.; Narayana Rao, K.

    2014-03-01

    The effective cost management during the conceptual design phase of a product is essential to develop a product with minimum cost and desired quality. The integration of the methodologies of quality function deployment (QFD), value engineering (VE) and target costing (TC) could be applied to the continuous improvement of any product during product development. To optimize customer satisfaction and total cost of a product, a mathematical model is established in this paper. This model integrates QFD, VE and TC under multi-objective optimization frame work. A case study on domestic refrigerator is presented to show the performance of the proposed model. Goal programming is adopted to attain the goals of maximum customer satisfaction and minimum cost of the product.

  16. A Weighted Multiobjective Optimization Method for Mixed-Model Assembly Line Problem

    Directory of Open Access Journals (Sweden)

    Şükran Şeker

    2013-01-01

    Full Text Available Mixed-model assembly line (MMAL is a type of assembly line where several distinct models of a product are assembled. MMAL is applied in many industrial environments today because of its greater variety in demand. This paper considers the objective of minimizing the work overload (i.e., the line balancing problem and station-to-station product flows. Generally, transportation time between stations are ignored in the literature. In this paper, Multiobjective Mixed-Integer Programming (MOMIP model is presented to optimize these two criteria simultaneously. Also, this MOMIP model incorporates a practical constraint that allows to add parallel stations to assembly line to decrease higher station time. In the last section, MOMIP is applied to optimize the cycle time and transportation time simultaneously in mixed-model assembly line of a real consumer electronics firm in Turkey, and computational results are presented.

  17. Simulated annealing algorithm for multi-objective optimization : application to electric motor design

    Energy Technology Data Exchange (ETDEWEB)

    Idoumghar, L. [Haute Alcace Univ., Mulhouse (France); Fodorean, D.; Mirraoui, A. [Univ. of Technology of Belfort-Montbeliard, Belfort (France). Dept. of Electrical Engineering and Control Systems

    2010-03-09

    Metaheuristics algorithms can solve complex optimization problems. A unique simulated annealing (SA) algorithm for multi-objective optimization was presented in this paper. The proposed SA algorithm was validated on five standard benchmark mathematical functions and improved the design of an inset permanent magnet motor with concentrated flux (IPMM-CF). The paper provided a description of the SA algorithm and discussed the results. The five benchmarks that were studied included Rastrigin's function; Rosenbrock's function; Michalewicz's function; Schwefel's function; and Noisy's function. The findings were also compared with results obtained by using the Ant Colony paradigm as well as with a particle swarm algorithm. Conclusions and further research options were also offered. It was concluded that the proposed approach has better performance in terms of accuracy, convergence rate, stability and robustness. 15 refs., 4 tabs., 9 figs.

  18. Multi-objective Optimization For The Dynamic Multi-Pickup and Delivery Problem with Time Windows

    CERN Document Server

    Dridi, Imen Harbaoui; Borne, Pierre; Ksouri, Mekki

    2011-01-01

    The PDPTW is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers satisfying precedence, capacity and time constraints. We present, in this paper, a genetic algorithm for multi-objective optimization of a dynamic multi pickup and delivery problem with time windows (Dynamic m-PDPTW). We propose a brief literature review of the PDPTW, present our approach based on Pareto dominance method and lower bounds, to give a satisfying solution to the Dynamic m-PDPTW minimizing the compromise between total travel cost and total tardiness time. Computational results indicate that the proposed algorithm gives good results with a total tardiness equal to zero with a tolerable cost.

  19. Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms

    Science.gov (United States)

    Severino, Bernardo; Gana, Felipe; Palma-Behnke, Rodrigo; Estévez, Pablo A.; Calderón-Muñoz, Williams R.; Orchard, Marcos E.; Reyes, Jorge; Cortés, Marcelo

    2014-12-01

    Lithium-battery energy storage systems (LiBESS) are increasingly being used on electric mobility and stationary applications. Despite its increasing use and improvements of the technology there are still challenges associated with cost reduction, increasing lifetime and capacity, and higher safety. A correct battery thermal management system (BTMS) design is critical to achieve these goals. In this paper, a general framework for obtaining optimal BTMS designs is proposed. Due to the trade-off between the BTMS's design goals and the complex modeling of thermal response inside the battery pack, this paper proposes to solve this problem using a novel Multi-Objective Particle Swarm Optimization (MOPSO) approach. A theoretical case of a module with 6 cells and a real case of a pack used in a Solar Race Car are presented. The results show the capabilities of the proposal methodology, in which improved designs for battery packs are obtained.

  20. 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.

  1. A genetic algorithm for the pareto optimal solution set of multi-objective shortest path problem

    Institute of Scientific and Technical Information of China (English)

    HU Shi-cheng; XU Xiao-fei; ZHAN De-chen

    2005-01-01

    Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the multi-objective shortest path problem (MSPP) has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algorithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this paper. The encoding of the solution and the operators such as crossover, mutation and selection are developed.The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.

  2. Multi-objective reservoir operation during flood season considering spillway optimization

    Science.gov (United States)

    Liu, Xinyuan; Chen, Lu; Zhu, Yonghui; Singh, Vijay P.; Qu, Geng; Guo, Xiaohu

    2017-09-01

    Flood control and hydropower generation are two main functions of Three Gorges Reservoir (TGR) in China. In this study, a multi-objective operation model for TGR considering these two functions was developed. Since the optimal results of reservoir operation are mostly in the form of gross outflow which is hardly used to directly guide reservoir operation, the optimization of spillways operation was taken into account. For observed historical flood hydrographs and design flood hydrographs, the progressive optimality algorithm (POA) was employed to determine the optimal operation of spillways. For the real-time reservoir operation, a smooth support vector machine (SSVM) model was applied to abstract the optimal operation rules which consider the order and the number of spillways put into use. Results demonstrate that the use of different spillways has a significant impact on reservoir operation. Therefore, it is necessary to consider the order and number of spillways that should be used. Instead of optimizing outflow, direct optimization of the order and number of spillways can yield most reasonable results. The SSVM model simulates the relationship among inflow, water level and outflow satisfactorily and can be used for real-time or short term reservoir operation. Application of the SSVM model can also reduce flood risk and increase hydropower generation during the flood season.

  3. Multi-objective Optimizations of a Novel Cryo-cooled DC Gun Based Ultra Fast Electron Diffraction Beamline

    OpenAIRE

    Gulliford, C.; Bartnik, A.; Bazarov, I.

    2015-01-01

    We present the results of multi-objective genetic algorithm optimizations of a potential single shot ultra fast electron diffraction beamline utilizing a 225 kV dc gun with a novel cryocooled photocathode system and buncher cavity. Optimizations of the transverse projected emittance as a function of bunch charge are presented and discussed in terms of the scaling laws derived in the charge saturation limit. Additionally, optimization of the transverse coherence length as a function of final r...

  4. Optimal Tuning of Decentralized PI Controller of Nonlinear Multivariable Process Using Archival Based Multiobjective Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    R. Kotteeswaran

    2014-01-01

    Full Text Available A Multiobjective Particle Swarm Optimization (MOPSO algorithm is proposed to fine-tune the baseline PI controller parameters of Alstom gasifier. The existing baseline PI controller is not able to meet the performance requirements of Alstom gasifier for sinusoidal pressure disturbance at 0% load. This is considered the major drawback of controller design. A best optimal solution for Alstom gasifier is obtained from a set of nondominated solutions using MOPSO algorithm. Performance of gasifier is investigated at all load conditions. The controller with optimized controller parameters meets all the performance requirements at 0%, 50%, and 100% load conditions. The investigations are also extended for variations in coal quality, which shows an improved stability of the gasifier over a wide range of coal quality variations.

  5. Reliability based multiobjective optimization for design of structures subject to random vibrations

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Based on a multiobjective approach whose objective function (OF) vector collects stochastic reliability performance and structural cost indices, a structural optimization criterion for mechanical systems subject to random vibrations is presented for supporting engineer's design. This criterion differs from the most commonly used conventional optimum design criterion for random vibrating structure, which is based on minimizing displacement or acceleration variance of main structure responses,without considering explicitly required performances against failure. The proposed criterion can properly take into account the design-reliability required performances, and it becomes a more efficient support for structural engineering decision making. The multiobjective optimum (MOO) design of a tuned mass damper (TMD) has been developed in a typical seismic design problem, to control structural vibration induced on a multi-storey building structure excited by nonstationary base acceleration random process.A numerical example for a three-storey building is developed and a sensitivity analysis is carried out. The results are shown in a useful manner for TMD design decision support.

  6. Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

    Science.gov (United States)

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

    2016-08-15

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Multi-objective Optimization for Common and Special Components: First Step Toward Network Optimization of Regular and Non-Regular Flights

    OpenAIRE

    Takahiro Jinba; Hiroto Kitagawa; Eriko Azuma; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama

    2015-01-01

    To optimize the problem composed of (i) the common components which should be optimized from the viewpoint of all objective functions and (ii) the special components which should be optimized from the viewpoint of one of the objective functions, this paper proposes a new multi-objective optimization method which optimizes not only the common components for all objective functions but also the special ones for each objective function. To investigate the effectiveness of the proposed method, th...

  8. 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.

  9. 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

    2016-06-01

    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.

  10. Pruning and ranking the Pareto optimal set, application for the dynamic multi-objective network design problem.

    NARCIS (Netherlands)

    Wismans, L.J.J.; Brands, T.; Berkum, van E.C.; Bliemer, M.C.J.

    2014-01-01

    Solving the multi-objective network design problem (MONDP) resorts to a Pareto optimal set. This set can provide additional information like trade-offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pa

  11. A Class of Optimization Method for Bilevel Multi-objective Decision Making Problem with the Help of Satisfactoriness

    Institute of Scientific and Technical Information of China (English)

    LI Tong; TENG Chun-xian; LI Hao-bai

    2002-01-01

    In the paper, it is discussed that the method on how to transform the multi-person bilevel multi-objective decision making problem into the equivalent generalized multi-objective decision making problem by using Kuhn-Tucker sufficient and necessary condition. In order to embody the decision maker's hope and transform it into single-objective decision making problem with the help of e-constraint method.Then we can obtain the global optimal solution by means of simulated annealing algorithm.

  12. A multi-objective approach to the design of low thrust space trajectories using optimal control

    Science.gov (United States)

    Dellnitz, Michael; Ober-Blöbaum, Sina; Post, Marcus; Schütze, Oliver; Thiere, Bianca

    2009-11-01

    In this article, we introduce a novel three-step approach for solving optimal control problems in space mission design. We demonstrate its potential by the example task of sending a group of spacecraft to a specific Earth L 2 halo orbit. In each of the three steps we make use of recently developed optimization methods and the result of one step serves as input data for the subsequent one. Firstly, we perform a global and multi-objective optimization on a restricted class of control functions. The solutions of this problem are (Pareto-)optimal with respect to Δ V and flight time. Based on the solution set, a compromise trajectory can be chosen suited to the mission goals. In the second step, this selected trajectory serves as initial guess for a direct local optimization. We construct a trajectory using a more flexible control law and, hence, the obtained solutions are improved with respect to control effort. Finally, we consider the improved result as a reference trajectory for a formation flight task and compute trajectories for several spacecraft such that these arrive at the halo orbit in a prescribed relative configuration. The strong points of our three-step approach are that the challenging design of good initial guesses is handled numerically by the global optimization tool and afterwards, the last two steps only have to be performed for one reference trajectory.

  13. Multiobjective Optimization of Low-Specific-Speed Multistage Pumps by Using Matrix Analysis and CFD Method

    Directory of Open Access Journals (Sweden)

    Qiaorui Si

    2013-01-01

    Full Text Available The implementation of energy-saving and emission-reduction techniques has become a worldwide consensus. Thus, special attention should be provided to the field of pump optimization. With the objective of focusing on multiobjective optimization problems in low-specific-speed pumps, 10 parameters were carefully selected in this study for an L27(310 orthogonal experiment. The parameters include the outlet width of the impeller blade, blade number, and inlet setting angle of the guide vane. The numerical calculation appropriate for forecasting the performance of multistage pumps, such as the head, efficiency, and shaft power, was analyzed. Results were obtained after calculating the two-stage flow field of the pump through computational fluid dynamics (CFD methods. A matrix method was proposed to optimize the results of the orthographic experiment. The optimal plan was selected according to the weight of each factor. Calculated results indicate that the inlet setting angle of the guide vane influences efficiency significantly and that the outlet angle of blades has an effect on the head and shaft power. A prototype was produced with the optimal plan for testing. The efficiency rating of the prototype reached 58.61%; maximum shaft power was within the design requirements, which verifies that the proposed method is feasible for pump optimization.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-06-15

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

  15. Multi-objective optimization strategies using adjoint method and game theory in aerodynamics

    Institute of Scientific and Technical Information of China (English)

    Zhili Tang

    2006-01-01

    There are currently three different game strategies originated in economics:(1) Cooperative games (Pareto front),(2)Competitive games (Nash game) and (3)Hierarchical games (Stackelberg game).Each game achieves different equilibria with different performance,and their players play different roles in the games.Here,we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multicriteria aerodynamic optimization problems.The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments.We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method.The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front.Non-dominated Pareto front solutions are obtained,however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

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

    Directory of Open Access Journals (Sweden)

    Mohammad H. Ahmadi

    2016-06-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

  18. The Effect of Aerodynamic Evaluators on the Multi-Objective Optimization of Flatback Airfoils

    Science.gov (United States)

    Miller, M.; Slew, K. Lee; Matida, E.

    2016-09-01

    With the long lengths of today's wind turbine rotor blades, there is a need to reduce the mass, thereby requiring stiffer airfoils, while maintaining the aerodynamic efficiency of the airfoils, particularly in the inboard region of the blade where structural demands are highest. Using a genetic algorithm, the multi-objective aero-structural optimization of 30% thick flatback airfoils was systematically performed for a variety of aerodynamic evaluators such as lift-to-drag ratio (Cl/Cd), torque (Ct), and torque-to-thrust ratio (Ct/Cn) to determine their influence on airfoil shape and performance. The airfoil optimized for Ct possessed a 4.8% thick trailing-edge, and a rather blunt leading-edge region which creates high levels of lift and correspondingly, drag. It's ability to maintain similar levels of lift and drag under forced transition conditions proved it's insensitivity to roughness. The airfoil optimized for Cl/Cd displayed relatively poor insensitivity to roughness due to the rather aft-located free transition points. The Ct/Cn optimized airfoil was found to have a very similar shape to that of the Cl/Cd airfoil, with a slightly more blunt leading-edge which aided in providing higher levels of lift and moderate insensitivity to roughness. The influence of the chosen aerodynamic evaluator under the specified conditions and constraints in the optimization of wind turbine airfoils is shown to have a direct impact on the airfoil shape and performance.

  19. Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

    Science.gov (United States)

    Holst, Terry L.; Pulliam, Thomas H.

    2003-01-01

    A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

  20. Multiobjective Optimization of Allocated Exchange Portfolio: Model and Solution—A Case Study in Iran

    Directory of Open Access Journals (Sweden)

    Mostafa Ekhtiari

    2014-01-01

    Full Text Available This paper presents a triobjective model for optimization of allocated exchange portfolio. The objectives of this model are minimizing risk and investment initial cost (by adopting two synchronic policies of buying and selling assets and maximizing return, to optimize allocated portfolios (APs. In an AP, an investor by considering previous investment experiences and market conditions selects the within portfolio assets. Then, considering proposed model, the assets proportion of AP is optimized for a limited time horizon. In optimizing a multiobjective problem of an AP, risk and return objectives are measured on the basis of standard deviation of assets dairy return and dairy return mean within AP assets, respectively. We present a set of interobjectives trade-offs along with an analysis of Iran Melli bank investment in an exchange AP, using Weighted Global Criterion (WGC method with assumption p=1, 2, and ∞ to optimize the proposed model. Results of WGC model (in all p=1, 2 and ∞ represent that US dollar exchange in comparison with other exchanges, was rather the fewest exchange proportion in Iran Melli bank exchange AP which this is consistent with Iran exchange investment policy of more concentration on other exchanges.

  1. Robust Airfoil Optimization with Multi-objective Estimation of Distribution Algorithm

    Institute of Scientific and Technical Information of China (English)

    Zhong Xiaoping; Ding Jifeng; Li Weiji; Zhang Yong

    2008-01-01

    A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditious. To overcome this shortcoming, robust design is proposed to fred out the optimal profile of an airfoil to maintain its performance in an uncertain environment. The robust airfoil optimization is aimed to minimize mean values and variances of drag coefficients while satisfying the lift and thickness constraints over a range of Maeb numbers. A multi-objective estimation of distribution algorithm is applied to the robust airfoil optimization on the base of the RAE2822 benchmark airfoil. The shape of the airfoil is obtained through superposing ten Hick-Heune shape functions upon the benchmark airfoil. A set of design points is selected according to a uniform design table for aerodynamic evaluation. A Kriging model of drag coefficient is coustrueted with those points to reduce eumputing costs. Over the Maeh range fi'om 0.7 to 0.8, the airfoil generated by the robust optimization has a configuration characterized by supercritical airfoil with low drag coefficients. The small fluctuation in its drag coefficients means that the performance of the robust airfoil is insensitive to variation of Mach number.

  2. MULTIOBJECTIVE FLEXIBLE JOB SHOP SCHEDULING USING A MODIFIED INVASIVE WEED OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Souad Mekni

    2015-02-01

    Full Text Available Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs with the criteria to minimize the maximum completion time (makespan, the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

  3. A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

    Directory of Open Access Journals (Sweden)

    Silvio Rodrigues

    2016-03-01

    Full Text Available Current offshore wind farms (OWFs design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.

  4. A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

    Science.gov (United States)

    Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.

    2015-08-01

    A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

  5. A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

    Science.gov (United States)

    Maringanti, C.; Chaubey, I.; Arabi, M.; Engel, B.

    2008-07-01

    Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs), such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine concentration reduction

  6. A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

    Directory of Open Access Journals (Sweden)

    C. Maringanti

    2008-07-01

    Full Text Available Pesticides (particularly atrazine used in corn fields are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs, such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II. The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine

  7. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Low-Thrust Mission Design

    Science.gov (United States)

    Englander, Jacob A.; Vavrina, Matthew A.; Ghosh, Alexander R.

    2015-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. In addition, a time-history of control variables must be chosen that defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on a hypothetical mission to the main asteroid belt.

  8. A multi-objective dynamic programming approach to constrained discrete-time optimal control

    Energy Technology Data Exchange (ETDEWEB)

    Driessen, B.J.; Kwok, K.S.

    1997-09-01

    This work presents a multi-objective differential dynamic programming approach to constrained discrete-time optimal control. In the backward sweep of the dynamic programming in the quadratic sub problem, the sub problem input at a stage or time step is solved for in terms of the sub problem state entering that stage so as to minimize the summed immediate and future cost subject to minimizing the summed immediate and future constraint violations, for all such entering states. The method differs from previous dynamic programming methods, which used penalty methods, in that the constraints of the sub problem, which may include terminal constraints and path constraints, are solved exactly if they are solvable; otherwise, their total violation is minimized. Again, the resulting solution of the sub problem is an input history that minimizes the quadratic cost function subject to being a minimizer of the total constraint violation. The expected quadratic convergence of the proposed algorithm is demonstrated on a numerical example.

  9. Multi-objective optimization design of rocker arm on crown-mounted compensator

    Science.gov (United States)

    Huang, Zhiqiang; Xu, Ziyang; Liang, Chunping; Mu, Xinming; Huang, Lingfeng

    2017-05-01

    Aiming to solve the severe wear of wire of rocker arm on crown-mounted compensator, the working principle of rocker mechanism is analyzed and mechanical properties of wire is calculated by a model based on Simulink. To reduce the contact stress amplitude between the wire rope and three pulleys on the rocker mechanism, the multi-objective optimization design model of rocker mechanism is constructed. In addition, the length of rocker and linkage arm are considered as design parameters by using MATLAB software. The results showed that the contact stress of wire rope decreased by 21.6%, when the length of rocker and linkage arm is respectively 3450mm and 5900mm, which significantly reduced the possibility of fatigue failure and the wear loss of wire rope.

  10. Multi-objective optimization based on Genetic Algorithm for PID controller tuning

    Institute of Scientific and Technical Information of China (English)

    WANG Guo-liang; YAN Wei-wu; SHAO Hui-he

    2009-01-01

    To get the satisfying performance of a PID controller, this paper presents a novel Pareto - based multi-objective genetic algorithm ( MOGA), which can be used to find the appropriate setting of the PID controller by analyzing the pareto optimal surfaces. Rated settings of the controller by two criteria, the error between output and reference signals and control moves, are listed on the pareto surface. Appropriate setting can be chosen under a balance between two criteria for different control purposes. A controller tuning problem for a plant with high order and time delay is chosen as an example. Simulation results show that the method of MOGA is more efficient compared with traditional tuning methods.

  11. Bus Timetabling as a Fuzzy Multiobjective Optimization Problem Using Preference-based Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Surafel Luleseged Tilahun

    2012-05-01

    Full Text Available Transportation plays a vital role in the development of a country and the car is the most commonly used means. However, in third world countries long waiting time for public buses is a common problem, especially when people need to switch buses. The problem becomes critical when one considers buses joining different villages and cities. Theoretically this problem can be solved by assigning more buses on the route, which is not possible due to economical problem. Another option is to schedule the buses so that customers who want to switch buses at junction cities need not have to wait long. This paper discusses how to model single frequency routes bus timetabling as a fuzzy multiobjective optimization problem and how to solve it using preference-based genetic algorithm by assigning appropriate fuzzy preference to the need of the customers. The idea will be elaborated with an example.

  12. Geodesic B-Preinvex Functions and Multiobjective Optimization Problems on Riemannian Manifolds

    Directory of Open Access Journals (Sweden)

    Sheng-lan Chen

    2014-01-01

    Full Text Available We introduce a class of functions called geodesic B-preinvex and geodesic B-invex functions on Riemannian manifolds and generalize the notions to the so-called geodesic quasi/pseudo B-preinvex and geodesic quasi/pseudo B-invex functions. We discuss the links among these functions under appropriate conditions and obtain results concerning extremum points of a nonsmooth geodesic B-preinvex function by using the proximal subdifferential. Moreover, we study a differentiable multiobjective optimization problem involving new classes of generalized geodesic B-invex functions and derive Kuhn-Tucker-type sufficient conditions for a feasible point to be an efficient or properly efficient solution. Finally, a Mond-Weir type duality is formulated and some duality results are given for the pair of primal and dual programming.

  13. A Multiobjective Optimization Framework for Routing in Wireless Ad Hoc Networks

    CERN Document Server

    Jaffrès-Runser, Katia; Gorce, Jean-Marie

    2009-01-01

    Wireless ad hoc networks are seldom characterized by one single performance metric, yet the current literature lacks a flexible framework to assist in characterizing the design tradeoffs in such networks. In this work, we address this problem by proposing a new modeling framework for routing in ad hoc networks, which used in conjunction with metaheuristic multiobjective search algorithms, will result in a better understanding of network behavior and performance when multiple criteria are relevant. Our approach is to take a holistic view of network management and control that captures the cross-interactions among interference management techniques implemented at various layers of the protocol stack. We present the Pareto optimal sets for an example sensor network when delay, robustness and energy are considered as performance criteria for the network.

  14. Multi-Objective Optimization for Equipment Capacity in Off-Grid Smart House

    Directory of Open Access Journals (Sweden)

    Yasuaki Miyazato

    2017-01-01

    Full Text Available Recently, the off-grid smart house has been attracting attention in Japan for considering global warming. Moreover, the selling price of surplus power from the renewable energy system by Feed-In Tariff (FIT has declined. Therefore, this paper proposes an off-grid smart house with the introduced Photovoltaic (PV system, Solar Collector (SC system, Hot Water Heat Pump (HWHP, fixed battery and Electric Vehicle (EV. In this research, a multi-objective optimization problem is considered to minimize the introduced capacity and shortage of the power supply in the smart house. It can perform the electric power procurement from the EV charging station for the compensation of a shortage of power supply. From the simulation results, it is shown that the shortage of the power supply can be reduced by the compensation of the EV power. Furthermore, considering the uncertainty for PV output power, reliable simulation results can be obtained.

  15. 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.

  16. On the Model Study of Multiobjective Optimization for Regulating Regional Economy

    Institute of Scientific and Technical Information of China (English)

    LUO Zhi-hui

    2001-01-01

    Based on different equation of Cobb-Douglas production function, the model of regulating regional economy on the multiobjective optimization of efficiency and equity is set up, with the spatial distribution strategy of capital and labour force as the controlling variable. Two basic conclusions drawn from the model are strictly demonstrated: ① A country's economic development is decided by the consistency between the regional distributive strategy of increment investment and the regional difference in marginal investment revenue. ② The couple targets of efficiency and equity are strictly in conflict with each other, if and only if the marginal investment revenue in the developed regions is higher than in the undeveloped,otherwise the targets are consistent.

  17. Energy Analysis and Multi-Objective Optimization of an Internal Combustion Engine-Based CHP System for Heat Recovery

    Directory of Open Access Journals (Sweden)

    Abdolsaeid Ganjehkaviri

    2014-10-01

    Full Text Available A comprehensive thermodynamic study is conducted of a diesel based Combined Heat and Power (CHP system, based on a diesel engine and an Organic Rankine Cycle (ORC. Present research covers both energy and exergy analyses along with a multi-objective optimization. In order to determine the irreversibilities in each component of the CHP system and assess the system performance, a complete parametric study is performed to investigate the effects of major design parameters and operating conditions on the system’s performance. The main contribution of the current research study is to conduct both exergy and multi-objective optimization of a system using different working fluid for low-grade heat recovery. In order to conduct the evolutionary based optimization, two objective functions are considered in the optimization; namely the system exergy efficiency, and the total cost rate of the system, which is a combination of the cost associated with environmental impact and the purchase cost of each component. Therefore, in the optimization approach, the overall cycle exergy efficiency is maximized satisfying several constraints while the total cost rate of the system is minimized. To provide a better understanding of the system under study, the Pareto frontier is shown for multi-objective optimization and also an equation is derived to fit the optimized point. In addition, a closed form relationship between exergy efficiency and total cost rate is derived.

  18. Evolutionary optimization of a hierarchical object recognition model.

    Science.gov (United States)

    Schneider, Georg; Wersing, Heiko; Sendhoff, Bernhard; Körner, Edgar

    2005-06-01

    A major problem in designing artificial neural networks is the proper choice of the network architecture. Especially for vision networks classifying three-dimensional (3-D) objects this problem is very challenging, as these networks are necessarily large and therefore the search space for defining the needed networks is of a very high dimensionality. This strongly increases the chances of obtaining only suboptimal structures from standard optimization algorithms. We tackle this problem in two ways. First, we use biologically inspired hierarchical vision models to narrow the space of possible architectures and to reduce the dimensionality of the search space. Second, we employ evolutionary optimization techniques to determine optimal features and nonlinearities of the visual hierarchy. Here, we especially focus on higher order complex features in higher hierarchical stages. We compare two different approaches to perform an evolutionary optimization of these features. In the first setting, we directly code the features into the genome. In the second setting, in analogy to an ontogenetical development process, we suggest the new method of an indirect coding of the features via an unsupervised learning process, which is embedded into the evolutionary optimization. In both cases the processing nonlinearities are encoded directly into the genome and are thus subject to optimization. The fitness of the individuals for the evolutionary selection process is computed by measuring the network classification performance on a benchmark image database. Here, we use a nearest-neighbor classification approach, based on the hierarchical feature output. We compare the found solutions with respect to their ability to generalize. We differentiate between a first- and a second-order generalization. The first-order generalization denotes how well the vision system, after evolutionary optimization of the features and nonlinearities using a database A, can classify previously unseen test

  19. Multi-objective optimization for hybrid fuel cells power system under uncertainty

    Science.gov (United States)

    Subramanyan, Karthik; Diwekar, Urmila M.; Goyal, Amit

    One of the major applications of fuel cells is as onsite stationary electric power plants. Several types of configurations have been hypothesized and tested for these kinds of applications at the conceptual level but hybrid power plants are one of the most efficient. These are designs that combine a fuel cell cycle with other thermodynamic cycles to provide higher efficiency. Generally, the heat rejected by the fuel cell at a higher temperature is used in a bottoming cycle to generate steam. In this work we are considering a conceptual design of a solid oxide fuel cell-proton exchange membrane (SOFC-PEM) fuel cell hybrid power plant [R. Geisbrecht, Compact Electrochemical Reformer Based on SOFC Technology, AIChE Spring National Meeting, Atlanta, GA, 2000] in which the high temperature SOFC fuel cell acts both as electricity producer and fuel reformer for the low temperature PEM fuel cell (PEMFC). The exhaust from the PEM fuel cell goes to a waste hydrogen burner and heat recovery steam generator that produces steam for further utilizations. Optimizing this conceptual design involves consideration of a number of objectives. The process should have low pollutant emissions as well as cost competitive with the existing technology. The solution of a multi-objective optimization problem is not a single solution but a complete non-dominated or Pareto set, which includes the alternatives representing potential compromise solutions among the objectives. This makes a range of choice available to decision makers and provides them with the trade-off information among the multiple objectives effectively. This paper presents the optimal trade-off design solutions or the Pareto set for this hybrid power plant through a multi-objective optimization framework. This hybrid technology is new and the system level models used for fuel cells performance have significant uncertainties in them. In this paper, we characterize these uncertainties and study the effect of these uncertainties

  20. Multi-objective optimization design of a high-speed PM machine supported by magnetic bearings

    Science.gov (United States)

    Han, Bangcheng; Xue, Qinghao; Liu, Xu; Wang, Kun

    2017-08-01

    This paper proposes an optimal design method of permanent magnet machine (PMM) with cylindrical permanent magnet supported by magnetic bearings. The objectives of optimization design are minimizing the rotor loss while maximizing the power density of the PMM as well as the 1st order nature frequency of the rotor, and the constraints are size, the strength safety factor and the phase current. A 30 kW, 48,000 r/min PMM designed by the multi-objective optimization method is proposed and the results indicate: the rotor loss is decreased from 393 W to 290 W (is reduced by 26.2%); the power density of the PMM is increased from 1.86 kW/kg to 2.19 kW/kg (is increased by 17.7%); the 1st order nature frequency of the rotor is increased from 1579 Hz to 1812 Hz (is increased by 14.7%). The performances of the PMM are improved after optimization, which are verified by experiment.

  1. Optimal air quality policies and health: a multi-objective nonlinear approach.

    Science.gov (United States)

    Relvas, Helder; Miranda, Ana Isabel; Carnevale, Claudio; Maffeis, Giuseppe; Turrini, Enrico; Volta, Marialuisa

    2017-05-01

    The use of modelling tools to support decision-makers to plan air quality policies is now quite widespread in Europe. In this paper, the Regional Integrated Assessment Tool (RIAT+), which was designed to support policy-maker decision on optimal emission reduction measures to improve air quality at minimum costs, is applied to the Porto Urban Area (Portugal). In addition to technological measures, some local measures were included in the optimization process. Case study results are presented for a multi-objective approach focused on both NO2 and PM10 control measures, assuming equivalent importance in the optimization process. The optimal set of air quality measures is capable to reduce simultaneously the annual average concentrations values of PM10 and NO2 in 1.7 and 1.0 μg/m(3), respectively. This paper illustrates how the tool could be used to prioritize policy objectives and help making informed decisions about reducing air pollution and improving public health.

  2. Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    周杰; 卓芳; 黄磊; 罗艳

    2015-01-01

    To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental (CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model (RSM) was set up and the results of the analysis of variance (ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the“trial and error”procedure.

  3. Multiobjective optimizations of a novel cryocooled dc gun based ultrafast electron diffraction beam line

    Science.gov (United States)

    Gulliford, Colwyn; Bartnik, Adam; Bazarov, Ivan

    2016-09-01

    We present the results of multiobjective genetic algorithm optimizations of a single-shot ultrafast electron diffraction beam line utilizing a 225 kV dc gun with a novel cryocooled photocathode system and buncher cavity. Optimizations of the transverse projected emittance as a function of bunch charge are presented and discussed in terms of the scaling laws derived in the charge saturation limit. Additionally, optimization of the transverse coherence length as a function of final rms bunch length at the sample location have been performed for three different sample radii: 50, 100, and 200 μ m , for two final bunch charges: 1 05 electrons (16 fC) and 1 06 electrons (160 fC). Example optimal solutions are analyzed, and the effects of disordered induced heating estimated. In particular, a relative coherence length of Lc ,x/σx=0.27 nm /μ m was obtained for a final bunch charge of 1 05 electrons and final bunch length of σt≈100 fs . For a final charge of 1 06 electrons the cryogun produces Lc ,x/σx≈0.1 nm /μ m for σt≈100 - 200 fs and σx≥50 μ m . These results demonstrate the viability of using genetic algorithms in the design and operation of ultrafast electron diffraction beam lines.

  4. Multi-objective optimization of crimping of large-diameter welding pipe

    Institute of Scientific and Technical Information of China (English)

    范利锋; 高颖; 云建斌; 李志鹏

    2015-01-01

    Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element (FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.

  5. A preference-based multi-objective model for the optimization of best management practices

    Science.gov (United States)

    Chen, Lei; Qiu, Jiali; Wei, Guoyuan; Shen, Zhenyao

    2015-01-01

    The optimization of best management practices (BMPs) at the watershed scale is notably complex because of the social nature of decision process, which incorporates information that reflects the preferences of decision makers. In this study, a preference-based multi-objective model was designed by modifying the commonly-used Non-dominated Sorting Genetic Algorithm (NSGA-II). The reference points, achievement scalarizing functions and an indicator-based optimization principle were integrated for searching a set of preferred Pareto-optimality solutions. Pareto preference ordering was also used for reducing objective numbers in the final decision-making process. This proposed model was then tested in a typical watershed in the Three Gorges Region, China. The results indicated that more desirable solutions were generated, which reduced the burden of decision effort of watershed managers. Compare to traditional Genetic Algorithm (GA), those preferred solutions were concentrated in a narrow region close to the projection point instead of the entire Pareto-front. Based on Pareto preference ordering, the solutions with the best objective function values were often the more desirable solutions (i.e., the minimum cost solution and the minimum pollutant load solution). In the authors' view, this new model provides a useful tool for optimizing BMPs at watershed scale and is therefore of great benefit to watershed managers.

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

    Directory of Open Access Journals (Sweden)

    Harun Alrasyid

    2011-05-01

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

  7. Design of AC-DC Grid Connected Converter using Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    Piasecki Szymon

    2014-05-01

    Full Text Available Power electronic circuits, in particular AC-DC converters are complex systems, many different parameters and objectives have to be taken into account during the design process. Implementation of Multi-Objective Optimization (MOO seems to be attractive idea, which used as designer supporting tool gives possibility for better analysis of the designed system. This paper presents a short introduction to the MOO applied in the field of power electronics. Short introduction to the subject is given in section I. Then, optimization process and its elements are briefly described in section II. Design procedure with proposed optimization parameters and performance indices for AC-DC Grid Connected Converter (GCC interfacing distributed systems is introduced in section III. Some preliminary optimization results, achieved on the basis of analytical and simulation study, are shown at each stage of designing process. Described optimization parameters and performance indices are part of developed global optimization method dedicated for ACDC GCC introduced in section IV. Described optimization method is under development and only short introduction and basic assumptions are presented. In section V laboratory prototype of high efficient and compact 14 kVA AC-DC converter is introduced. The converter is elaborated based on performed designing and optimization procedure with the use of silicon carbide (SiC power semiconductors. Finally, the paper is summarized and concluded in section VI. In presented work theoretical research are conducted in parallel with laboratory prototyping e.g. all theoretical ideas are verified in laboratory using modern DSP microcontrollers and prototypes of the ACDC GCC.

  8. 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...... variables are the working fluid, the turbine inlet pressure and temperature, the condensing temperature, the pinch points and the fluid velocities in the heat exchangers. The optimization process also includes the complete design of the shell and tube heat exchangers utilized in the organic Rankine cycle...

  9. Multi-objective optimization of bioethanol production during cold enzyme starch hydrolysis in very high gravity cassava mash.

    Science.gov (United States)

    Yingling, Bao; Li, Chen; Honglin, Wang; Xiwen, Yu; Zongcheng, Yan

    2011-09-01

    Cold enzymatic hydrolysis conditions for bioethanol production were optimized using multi-objective optimization. Response surface methodology was used to optimize the effects of α-amylase, glucoamylase, liquefaction temperature and liquefaction time on S. cerevisiae biomass, ethanol concentration and starch utilization ratio. The optimum hydrolysis conditions were: 224 IU/g(starch) α-amylase, 694 IU/g(starch) glucoamylase, 77°C and 104 min for biomass; 264 IU/g(starch) α-amylase, 392 IU/g(starch) glucoamylase, 60°C and 85 min for ethanol concentration; 214 IU/g(starch) α-amylase, 398 IU/g(starch) glucoamylase, 79°C and 117 min for starch utilization ratio. The hydrolysis conditions were subsequently evaluated by multi-objectives optimization utilizing the weighted coefficient methods. The Pareto solutions for biomass (3.655-4.380×10(8)cells/ml), ethanol concentration (15.96-18.25 wt.%) and starch utilization ratio (92.50-94.64%) were obtained. The optimized conditions were shown to be feasible and reliable through verification tests. This kind of multi-objective optimization is of potential importance in industrial bioethanol production.

  10. 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

  11. Research on Multiobjective Optimization Problem%多目标优化问题的研究

    Institute of Scientific and Technical Information of China (English)

    朱君; 蔡延光; 汤雅连; 杨军

    2014-01-01

    Aiming at the limitation of the traditional methods to solve the multi-objective optimization problem , this paper applies a new kind of algorithm to it .Genetic Algorithm ( GA) starts to search from the set of solution with its big coverage able to handle more than one individual at the same time , beneficial to global optimization , reducing the risk of fall into local optimum , while the minimum spanning tree has the characteristics of simple process and wide applicability .Combined with the advantages of the both algorithms , genetic algorithm is constructed on the basis of spanning tree .By finding the optimal solution by weighted goal programming method , and then solving the problem by the genetic algorithm and genetic algorithm based on spanning tree , the result shows that for small-scale multi-objective optimization problem , two algorithms can find out the optimal solution , and in terms of sol-ving time, genetic algorithm based on spanning tree is superior to genetic algorithm .%针对传统方法求解多目标优化问题的局限性,应用一种新的算法求解。遗传算法从问题解的串集开始搜索,覆盖面大,可以同时处理群体中的多个个体,利于全局择优,减少陷入局部最优的风险,而最小生成树具有过程简单清晰、适用性广泛的特点,结合两者的优点,构造了基于生成树的遗传算法。首先通过加权目标规划法求出最优解,然后通过遗传算法和基于生成树的遗传算法求解,结果表明,对于小规模的多目标优化问题,两种算法都可以求出最优解,在求解时间方面,基于生成树的遗传算法比遗传算法更优越。

  12. A Comparative Study of Multi-Objective Optimization Algorithms for Automatic Calibration

    Science.gov (United States)

    Asadzadeh, M.; Tolson, B.; Maclean, A.

    2009-12-01

    Hydrologic model calibration is often a computationally expensive problem that aims to find a set of parameters that simulates observations. It has been shown that no single metric can comprehensively evaluate the effectiveness of the calibration. Moreover, many of the proposed metrics are conflicting (e.g., the set of parameters that achieves accurate high flow predictions is different from the set of parameters that achieves accurate low flow predictions). Conflict is even more likely when objectives are based on different fluxes and/or state variables (e.g., streamflow versus Snow Water Equivalent (SWE)). The goal of solving a multi-objective optimization problem is to approximate the tradeoff between objectives (also called the Pareto front) that represents the attained level of each metric in comparison with other metrics and hence helps to decide on the acceptable set of parameters. In this study, a variety of algorithms are applied to solve a multi-objective (MO) model calibration problem and the performance of these algorithms is compared. The calibration case study is the MESH model (a combined land surface and hydrologic model under development by Environment Canada) applied to the Reynolds Creek Experimental Watershed. MESH is calibrated against two objectives to adequately simulate the measured streamflow and SWE. The MO algorithms applied to this calibration problem include NSGAII, SPEA2 and AMALGAM. In addition, a new MO algorithm called the Pareto Archived Dynamically Dimensioned Search (PA-DDS) is also applied. PA-DDS uses DDS as a search engine and archives all the non-dominated solutions during the search. It inherits the parsimonious characteristic of DDS, so it has only one algorithm parameter which does not need tuning. This characteristic makes PA-DDS very suitable for solving multi-objective hydrologic model calibrations, since tuning the algorithm parameters in computationally intensive models is a very time consuming process. Preliminary

  13. Multi-Objective Optimization for Trustworthy Tactical Networks: A Survey and Insights

    Science.gov (United States)

    2013-06-01

    Hierarchical C2 Structure. Various types of auction -based algorithms have been proposed to solve coalition formation problems in the literature such... algorithm ε-constraints swarm optimization auction theory cooperative game theory game theoretic others # of works 0 2 4 6 8 10 min. workload...based on evolutionary algorithms or game theoretic approaches. However, there has been no generic framework to consider MOO problems in tactical networks

  14. Multiobjective optimization design of an rf gun based electron diffraction beam line

    Science.gov (United States)

    Gulliford, Colwyn; Bartnik, Adam; Bazarov, Ivan; Maxson, Jared

    2017-03-01

    Multiobjective genetic algorithm optimizations of a single-shot ultrafast electron diffraction beam line comprised of a 100 MV /m 1.6-cell normal conducting rf (NCRF) gun, as well as a nine-cell 2 π /3 bunching cavity placed between two solenoids, have been performed. These include optimization of the normalized transverse emittance as a function of bunch charge, as well as optimization of the transverse coherence length as a function of the rms bunch length of the beam at the sample location for a fixed charge of 1 06 electrons. Analysis of the resulting solutions is discussed in terms of the relevant scaling laws, and a detailed description of one of the resulting solutions from the coherence length optimizations is given. For a charge of 1 06 electrons and final beam sizes of σx≥25 μ m and σt≈5 fs , we found a relative coherence length of Lc ,x/σx≈0.07 using direct optimization of the coherence length. Additionally, based on optimizations of the emittance as a function of final bunch length, we estimate the relative coherence length for bunch lengths of 30 and 100 fs to be roughly 0.1 and 0.2 nm /μ m , respectively. Finally, using the scaling of the optimal emittance with bunch charge, for a charge of 1 05 electrons, we estimate relative coherence lengths of 0.3, 0.5, and 0.92 nm /μ m for final bunch lengths of 5, 30 and 100 fs, respectively.

  15. Multi-objective shape optimization of runner blade for Kaplan turbine

    Science.gov (United States)

    Semenova, A.; Chirkov, D.; Lyutov, A.; Chemy, S.; Skorospelov, V.; Pylev, I.

    2014-03-01

    Automatic runner shape optimization based on extensive CFD analysis proved to be a useful design tool in hydraulic turbomachinery. Previously the authors developed an efficient method for Francis runner optimization. It was successfully applied to the design of several runners with different specific speeds. In present work this method is extended to the task of a Kaplan runner optimization. Despite of relatively simpler blade shape, Kaplan turbines have several features, complicating the optimization problem. First, Kaplan turbines normally operate in a wide range of discharges, thus CFD analysis of each variant of the runner should be carried out for several operation points. Next, due to a high specific speed, draft tube losses have a great impact on the overall turbine efficiency, and thus should be accurately evaluated. Then, the flow in blade tip and hub clearances significantly affects the velocity profile behind the runner and draft tube behavior. All these features are accounted in the present optimization technique. Parameterization of runner blade surface using 24 geometrical parameters is described in details. For each variant of runner geometry steady state three-dimensional turbulent flow computations are carried out in the domain, including wicket gate, runner, draft tube, blade tip and hub clearances. The objectives are maximization of efficiency in best efficiency and high discharge operation points, with simultaneous minimization of cavitation area on the suction side of the blade. Multiobjective genetic algorithm is used for the solution of optimization problem, requiring the analysis of several thousands of runner variants. The method is applied to optimization of runner shape for several Kaplan turbines with different heads.

  16. Optimal DVB-S2 spectral efficiency with hierarchical modulation

    OpenAIRE

    Meric, Hugo

    2014-01-01

    We study the design of a DVB-S2 system in order to maximise spectral efficiency. This task is usually challenging due to channel variability. The solution adopted in modern satellite communications systems such as DVB-SH and DVB-S2 relies mainly on a time sharing strategy. Recently, we proposed to combine time sharing with hierarchical modulation to increase the transmission rate of broadcast systems. However, the optimal spectral efficiency remained an open question. In this paper, we show t...

  17. A Multiobjective Optimization Model of Production-Sourcing for Sustainable Supply Chain with Consideration of Social, Environmental, and Economic Factors

    Directory of Open Access Journals (Sweden)

    Zhixiang Chen

    2014-01-01

    Full Text Available This paper incorporates the three pillars of sustainability—economic, environmental, and social dimensions—into a supply chain. A multiobjective programming model which jointly minimizes costs, emissions, and employee injuries in a supply chain is first constructed. Using the weighted-sum approach with weights setting by the analytic hierarchy process (AHP, the model is solved by normalization of the minima of the three objectives. A numerical example is conducted to test the model. The results show that it is indeed possible to integrate environmental and social metrics in supply chain system optimization. Multiobjective optimization can balance the social, environmental, and economic performance. This paper presents a new multidimension perspective for optimizing supply chain; it will inspire practitioners to change their decision ideas and improve supply chain sustainability.

  18. Multiobjective Optimization of Steering Mechanism for Rotary Steering System Using Modified NSGA-II and Fuzzy Set Theory

    Directory of Open Access Journals (Sweden)

    Hongtao Li

    2015-01-01

    Full Text Available Due to the complicated design process of gear train, optimization is a significant approach to improve design efficiency. However, the design of gear train is a complex multiobjective optimization with mixed continuous-discrete variables under numerous nonlinear constraints, and conventional optimization algorithms are not suitable to deal with such optimization problems. In this paper, based on the established dynamic model of steering mechanism for rotary steering system, the key component of which is a planetary gear set with teeth number difference, the optimization problem of steering mechanism is formulated to achieve minimum dynamic responses and outer diameter by optimizing structural parameters under geometric, kinematic, and strength constraints. An optimization procedure based on modified NSGA-II by incorporating dynamic crowding distance strategies and fuzzy set theory is applied to the multiobjective optimization. For comparative purpose, NSGA-II is also employed to obtain Pareto optimal set, and dynamic responses of original and optimized designs are compared. The results show the optimized design has better dynamic responses with minimum outer diameter and the response decay decreases faster. The optimization procedure is feasible to the design of gear train, and this study can provide guidance for designer at the preliminary design phase of mechanical structures with gear train.

  19. Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

    Science.gov (United States)

    Akhtar, Taimoor; Shoemaker, Christine

    2016-04-01

    Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual

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

    Directory of Open Access Journals (Sweden)

    M. Balasubbareddy

    2015-12-01

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

  1. Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

    Directory of Open Access Journals (Sweden)

    Bo Liu

    2016-05-01

    Full Text Available The objective of the study is to present the modeling and multi-objective optimization of NOx conversion efficiency and NH3 slip in the Selective Catalytic Reduction (SCR catalytic converter for a diesel engine. A novel ensemble method based on a support vector machine (SVM and genetic algorithm (GA is proposed to establish the models for the prediction of upstream and downstream NOx emissions and NH3 slip. The data for modeling were collected from a steady-state diesel engine bench calibration test. After obtaining the two conflicting objective functions concerned in this study, the non-dominated sorting genetic algorithm (NSGA-II was implemented to solve the multi-objective optimization problem of maximizing NOx conversion efficiency while minimizing NH3 slip under certain operating points. The optimized SVM models showed great accuracy for the estimation of actual outputs with the Root Mean Squared Error (RMSE of upstream and downstream NOx emissions and NH3 slip being 44.01 × 10−6, 21.87 × 10−6 and 2.22 × 10−6, respectively. The multi-objective optimization and subsequent decisions for optimal performance have also been presented.

  2. Multiobjective Genetic Algorithms Program for the Optimization of an OTA for Front-End Electronics

    Directory of Open Access Journals (Sweden)

    Abdelghani Dendouga

    2014-01-01

    Full Text Available The design of an interface to a specific sensor induces costs and design time mainly related to the analog part. So to reduce these costs, it should have been standardized like digital electronics. The aim of the present work is the elaboration of a method based on multiobjectives genetic algorithms (MOGAs to allow automated synthesis of analog and mixed systems. This proposed methodology is used to find the optimal dimensional transistor parameters (length and width in order to obtain operational amplifier performances for analog and mixed CMOS-(complementary metal oxide semiconductor- based circuit applications. Six performances are considered in this study, direct current (DC gain, unity-gain bandwidth (GBW, phase margin (PM, power consumption (P, area (A, and slew rate (SR. We used the Matlab optimization toolbox to implement the program. Also, by using variables obtained from genetic algorithms, the operational transconductance amplifier (OTA is simulated by using Cadence Virtuoso Spectre circuit simulator in standard TSMC (Taiwan Semiconductor Manufacturing Company RF 0.18 μm CMOS technology. A good agreement is observed between the program optimization and electric simulation.

  3. Seeking sustainability: multiobjective evolutionary optimization for urban wastewater reuse in China.

    Science.gov (United States)

    Zhang, Wenlong; Wang, Chao; Li, Yi; Wang, Peifang; Wang, Qing; Wang, Dawei

    2014-01-21

    Sustainable design and implementation of wastewater reuse in China have to achieve an optimum compromise among water resources augmenting, pollutants reduction and economic profit. A systematic framework with a multiobjective optimization model is first developed considering the trade-offs among wastewater reuse supplies and demands, costs and profits, as well as pollutants reduction. Pareto fronts of wastewater reuse optimization for 31 provinces of China are obtained through nondominated sorting genetic algorithm trials. The control strategies for each province are selected on the basis of regional water resources and water environment status. On the national level, the control strategies of wastewater reuse scale, BOD5 reduction, and economic profit are 15.39 billion cubic meters, 176.31 kilotons, and 9.68 billion RMB Yuan, respectively. The driving forces of water resources augmenting and water pollution control play more important roles than economic profit during wastewater reuse expanding in China. According to the optimal allocations, reclaimed wastewater should be intensively used in municipal, domestic, and recreative sectors in the regions suffering from quantity-related water scarcity, while it should be focused on industrial users in the regions suffering from quality-related water scarcity. The results present a general picture of wastewater reuse for policy makers in China.

  4. Multi-objective Optimizations of a Normal Conducting RF Gun Based Ultra Fast Electron Diffraction Beamline

    CERN Document Server

    Gulliford, C; Maxson, J; Bazarov, I

    2016-01-01

    We present the results of multi-objective genetic algorithm optimizations of a potential single shot ultra fast electron diffraction beamline utilizing a 100 MV/m 1.6 cell normal conducting rf (NCRF) gun, as well as a 9 cell 2pi/3 bunching cavity placed between two solenoids. Optimizations of the transverse projected emittance as a function of bunch charge are presented and discussed in terms of the scaling laws derived in the charge saturation limit. Additionally, optimization of the transverse coherence length as a function of final rms bunch length at the sample location have been performed for a charge of 1e6 electrons. Analysis of the solutions is discussed, as are the effects of disorder induced heating. In particular, for a charge of $10^6$ electrons and final beam size greater than or equal to 25 microns, we found a relative coherence length of 0.07, 0.1, and 0.2 nm/micron for a final bunch length of approximately 5, 30, and 100 fs, respectively. These results demonstrate the viability of using geneti...

  5. Multi-objective optimal design of high frequency probe for scanning ion conductance microscopy

    Science.gov (United States)

    Guo, Renfei; Zhuang, Jian; Ma, Li; Li, Fei; Yu, Dehong

    2016-01-01

    Scanning ion conductance microscopy(SICM) is an emerging non-destructive surface topography characterization apparatus with nanoscale resolution. However, the low regulating frequency of probe in most existing modulated current based SICM systems increases the system noise, and has difficulty in imaging sample surface with steep height changes. In order to enable SICM to have the capability of imaging surfaces with steep height changes, a novel probe that can be used in the modulated current based hopping mode is designed. The design relies on two piezoelectric ceramics with different travels to separate position adjustment and probe frequency regulation in the Z direction. To further improve the resonant frequency of the probe, the material and the key dimensions for each component of the probe are optimized based on the multi-objective optimization method and the finite element analysis. The optimal design has a resonant frequency of above 10 kHz. To validate the rationality of the designed probe, microstructured grating samples are imaged using the homebuilt modulated current based SICM system. The experimental results indicate that the designed high frequency probe can effectively reduce the spike noise by 26% in the average number of spike noise. The proposed design provides a feasible solution for improving the imaging quality of the existing SICM systems which normally use ordinary probes with relatively low regulating frequency.

  6. Flood frequency analysis using multi-objective optimization based interval estimation approach

    Science.gov (United States)

    Kasiviswanathan, K. S.; He, Jianxun; Tay, Joo-Hwa

    2017-02-01

    Flood frequency analysis (FFA) is a necessary tool for water resources management and water infrastructure design. Owing to the existence of variability in sample representation, distribution selection, and distribution parameter estimation, flood quantile estimation is subjected to various levels of uncertainty, which is not negligible and avoidable. Hence, alternative methods to the conventional approach of FFA are desired for quantifying the uncertainty such as in the form of prediction interval. The primary focus of the paper was to develop a novel approach to quantify and optimize the prediction interval resulted from the non-stationarity of data set, which is reflected in the distribution parameters estimated, in FFA. This paper proposed the combination of the multi-objective optimization approach and the ensemble simulation technique to determine the optimal perturbations of distribution parameters for constructing the prediction interval of flood quantiles in FFA. To demonstrate the proposed approach, annual maximum daily flow data collected from two gauge stations on the Bow River, Alberta, Canada, were used. The results suggest that the proposed method can successfully capture the uncertainty in quantile estimates qualitatively using the prediction interval, as the number of observations falling within the constructed prediction interval is approximately maximized while the prediction interval is minimized.

  7. Multiobjective Optimization of ELID Grinding Process Using Grey Relational Analysis Coupled with Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    S. Prabhu

    2014-06-01

    Full Text Available Carbon nanotube (CNT mixed grinding wheel has been used in the electrolytic in-process dressing (ELID grinding process to analyze the surface characteristics of AISI D2 Tool steel material. CNT grinding wheel is having an excellent thermal conductivity and good mechanical property which is used to improve the surface finish of the work piece. The multiobjective optimization of grey relational analysis coupled with principal component analysis has been used to optimize the process parameters of ELID grinding process. Based on the Taguchi design of experiments, an L9 orthogonal array table was chosen for the experiments. The confirmation experiment verifies the proposed that grey-based Taguchi method has the ability to find out the optimal process parameters with multiple quality characteristics of surface roughness and metal removal rate. Analysis of variance (ANOVA has been used to verify and validate the model. Empirical model for the prediction of output parameters has been developed using regression analysis and the results were compared for with and without using CNT grinding wheel in ELID grinding process.

  8. Heuristics for Multiobjective Optimization of Two-Sided Assembly Line Systems

    Directory of Open Access Journals (Sweden)

    N. Jawahar

    2014-01-01

    Full Text Available Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E. Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA to handle problems of small and medium size and Simulated Annealing Algorithm (SAA for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems.

  9. A Multi-Objective Optimization Technique to Model the Pareto Front of Organic Dielectric Polymers

    Science.gov (United States)

    Gubernatis, J. E.; Mannodi-Kanakkithodi, A.; Ramprasad, R.; Pilania, G.; Lookman, T.

    Multi-objective optimization is an area of decision making that is concerned with mathematical optimization problems involving more than one objective simultaneously. Here we describe two new Monte Carlo methods for this type of optimization in the context of their application to the problem of designing polymers with more desirable dielectric and optical properties. We present results of applying these Monte Carlo methods to a two-objective problem (maximizing the total static band dielectric constant and energy gap) and a three objective problem (maximizing the ionic and electronic contributions to the static band dielectric constant and energy gap) of a 6-block organic polymer. Our objective functions were constructed from high throughput DFT calculations of 4-block polymers, following the method of Sharma et al., Nature Communications 5, 4845 (2014) and Mannodi-Kanakkithodi et al., Scientific Reports, submitted. Our high throughput and Monte Carlo methods of analysis extend to general N-block organic polymers. This work was supported in part by the LDRD DR program of the Los Alamos National Laboratory and in part by a Multidisciplinary University Research Initiative (MURI) Grant from the Office of Naval Research.

  10. A Novel Multiobjective Optimization Algorithm for Home Energy Management System in Smart Grid

    Directory of Open Access Journals (Sweden)

    Yanyu Zhang

    2015-01-01

    Full Text Available Demand response (DR is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar and plug-in hybrid electric vehicles, and strengthen the reliability of power system. In smart grid, implementing DR through home energy management system (HEMS in residential sector has a great significance. However, an algorithm that only optimally controls parts of HEMS rather than the overall system cannot obtain the best results. In addition, single objective optimization algorithm that minimizes electricity cost cannot quantify user’s comfort level and cannot take a tradeoff between electricity cost and comfort level conveniently. To tackle these problems, this paper proposes a framework of HEMS that consists of grid, load, renewable resource (i.e., solar resource, and battery. In this framework, a user has the ability to sell electricity to utility grid for revenue. Different comfort level indicators are proposed for different home appliances according to their characteristics and user preferences. Based on these comfort level indicators, this paper proposes a multiobjective optimization algorithm for HEMS that minimizes electricity cost and maximizes user’s comfort level simultaneously. Simulation results indicate that the algorithm can reduce user’s electricity cost significantly, ensure user’s comfort level, and take a tradeoff between the cost and comfort level conveniently.

  11. Multi-objective optimization of the dry electric discharge machining process

    CERN Document Server

    Saha, Sourabh

    2009-01-01

    Dry Electric Discharge Machining (EDM) is an environment-friendly modification of the conventional EDM process, which is obtained by replacing the liquid dielectric by a gaseous medium. In this study, multi-objective optimization of dry EDM process has been done using the non dominated sorting genetic algorithm (NSGA II), with material removal rate (MRR) and surface roughness (Ra) as the objective functions. Experiments were conducted with air as dielectric to develop polynomial models of MRR and Ra in terms of the six input parameters: gap voltage, discharge current, pulse-on time, duty factor, air pressure and spindle speed. A Pareto-optimal front was then obtained using NSGA II. Analysis of the front was done to identify separate regions for finish and rough machining. Designed experiments were then conducted in these focused regions to verify the optimization results and to identify the region-specific characteristics of the process. Finishing conditions were obtained at low current, high pulse-on time an...

  12. Multi-Objective Optimization Design for Cooling Unit of Automotive Exhaust-Based Thermoelectric Generators

    Science.gov (United States)

    Qiang, J. W.; Yu, C. G.; Deng, Y. D.; Su, C. Q.; Wang, Y. P.; Yuan, X. H.

    2016-03-01

    In order to improve the performance of cooling units for automotive thermoelectric generators, a study is carried out to optimize the cold side and the fin distributions arranged on its inner faces. Based on the experimental measurements and numerical simulations, a response surface model of different internal structures is built to analyze the heat transfer and pressure drop characteristics of fluid flow in the cooling unit. For the fin distributions, five independent variables including height, length, thickness, space and distance from walls are considered. An experimental study design incorporating the central composite design method is used to assess the influence of fin distributions on the temperature field and the pressure drop in the cooling units. The archive-based micro genetic algorithm (AMGA) is used for multi-objective optimization to analyze the sensitivity of the design variables and to build a database from which to construct the surrogate model. Finally, improvement measures are proposed for optimization of the cooling system and guidelines are provided for future research.

  13. Multi-Objective Reactive Power Optimization of Distribution Network with Distributed Generation

    Directory of Open Access Journals (Sweden)

    Zhao Hui

    2016-01-01

    Full Text Available Distributed generation (DG is considered to be a very promising alternative of power generation because of its tremendous environmental, social, and economic benefits. But the randomness and intermittent of DGs brings new problems to the system. This paper analyzes the reactive power optimization problem of distribution network with correlative DGs based on scenario analysis method. A new scenario division rule according to the joint distribution function of wind-PV power outputs is proposed in the paper. Then a multi-objective reactive power optimization model whose objects include the minimum active power losses, the minimum voltage deviation and the maximum static voltage stability margin is established. Non-dominated sorting genetic algorithm-II is used to solve the model. At the last of the paper, the model and the algorithm proposed are verified with an improved IEEE 33-bus system. The results show that the model will be a reference to the reactive power optimization problem in distribution system.

  14. Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

    Directory of Open Access Journals (Sweden)

    Jianfei Ye

    2015-01-01

    Full Text Available In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

  15. Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array

    Science.gov (United States)

    Abdul Rani, Khairul Najmy; Abdulmalek, Mohamedfareq; A. Rahim, Hasliza; Siew Chin, Neoh; Abd Wahab, Alawiyah

    2017-04-01

    This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler-Deb-Thiele’s (ZDT’s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously.

  16. Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Khosla, Dinesh K.; Gupta, Santosh K. [Department of Chemical Engineering, Indian Institute of Technology, Kanpur 208016 (India); Saraf, Deoki N. [Department of Chemical Engineering, University of Petroleum and Energy Studies, Dehradun 248007 (India)

    2007-01-15

    Production and marketing of heavy fuel oil (HFO) are an easy, effective and economical way to dispose off certain very heavy refinery streams such as short residue (SR, available from the bottom of vacuum distillation units) and clarified liquid oil (CLO, available from the bottom of the main fractionators of fluidized-bed catalytic crackers). Certain lighter streams such as heavy cycle oil (HCO), light cycle oil (LCO) and kerosene, are added to the heavy residual stock to improve its quality in terms of fluidity, combustibility, etc., to be marketed as fuel oil. The present study aims at optimization of the fuel oil blending process to maximize profit, minimize quality give-away, maximize production, minimize use of lighter products such as LCO and kerosene, and maximize the calorific value, etc. Several multi-objective optimization problems have been formulated comprising of two and three-objective functions and solved using the elitist non-dominated sorting genetic algorithm (NSGA-II). This evolutionary technique produces a set of non-dominating (equally good) Pareto optimal solutions from which the operator can choose the one that is most suitable (preferred point). Also, a fixed-length macro-macro mutation operator, inspired by jumping genes in natural genetics, has been used with NSGA-II to solve this problem. This modified algorithm leads to a significant reduction in the computational effort. Indeed, this adaptation can be of immense use in reducing the computational effort for other problems in chemical engineering. (author)

  17. Multi-objective Optimization of Coal-fired Boiler Combustion Based on NSGA-II

    Directory of Open Access Journals (Sweden)

    Tingfang Yu

    2013-06-01

    Full Text Available NOx emission characteristics and overall heat loss model for a 300MW coal-fired boiler were established by Back Propagation (BP neural network, by which the the functional relationship between outputs (NOx emissions & overall heat loss of the boiler and inputs (operational parameters of the boiler of a coal-fired boiler can be predicted. A number of field test data from a full-scale operating 300MWe boiler were used to train and verify the BP model. The NOx emissions & heat loss predicted by the BP neural network model showed good agreement with the measured. Then, BP model and the non-dominated sorting genetic algorithm II (NSGA-II were combined to gain the optimal operating parameters which lead to lower NOx emissions and overall heat loss boiler. The optimization results showed that hybrid algorithm by combining BP neural network with NSGA-II can be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion, which can reduce NOx emissions and overall heat loss effectively for the coal-fired boiler.

  18. Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    Cheng Lin

    2015-04-01

    Full Text Available The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB® and Carsim® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption.

  19. Sensor Networks Hierarchical Optimization Model for Security Monitoring in High-Speed Railway Transport Hub

    Directory of Open Access Journals (Sweden)

    Zhengyu Xie

    2015-01-01

    Full Text Available We consider the sensor networks hierarchical optimization problem in high-speed railway transport hub (HRTH. The sensor networks are optimized from three hierarchies which are key area sensors optimization, passenger line sensors optimization, and whole area sensors optimization. Case study on a specific HRTH in China showed that the hierarchical optimization method is effective to optimize the sensor networks for security monitoring in HRTH.

  20. Multi-Objective Distribution Network Operation Based on Distributed Generation Optimal Placement Using New Antlion Optimizer Considering Reliability

    Directory of Open Access Journals (Sweden)

    KHANBABAZADEH Javad

    2016-10-01

    Full Text Available Distribution network designers and operators are trying to deliver electrical energy with high reliability and quality to their subscribers. Due to high losses in the distribution systems, using distributed generation can improves reliability, reduces losses and improves voltage profile of distribution network. Therefore, the choice of the location of these resources and also determining the amount of their generated power to maximize the benefits of this type of resource is an important issue which is discussed from different points of view today. In this paper, a new multi-objective optimal location and sizing of distributed generation resources is performed to maximize its benefits on the 33 bus distribution test network considering reliability and using a new Antlion Optimizer (ALO. The benefits for DG are considered as system losses reduction, system reliability improvement and benefits from the sale electricity and voltage profile improvement. For each of the mentioned benefits, the ALO algorithm is used to optimize the location and sizing of distributed generation resources. In order to verify the proposed approach, the obtained results have been analyzed and compared with the results of particle swarm optimization (PSO algorithm. The results show that the ALO has shown better performance in optimization problem solution versus PSO.

  1. Multi-objective global optimization of a butterfly valve using genetic algorithms.

    Science.gov (United States)

    Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio

    2016-07-01

    A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.

  2. Multi-objective optimization of gear forging process based on adaptive surrogate meta-models

    Science.gov (United States)

    Meng, Fanjuan; Labergere, Carl; Lafon, Pascal; Daniel, Laurent

    2013-05-01

    In forging industry, net shape or near net shape forging of gears has been the subject of considerable research effort in the last few decades. So in this paper, a multi-objective optimization methodology of net shape gear forging process design has been discussed. The study is mainly done in four parts: building parametric CAD geometry model, simulating the forging process, fitting surrogate meta-models and optimizing the process by using an advanced algorithm. In order to maximally appropriate meta-models of the real response, an adaptive meta-model based design strategy has been applied. This is a continuous process: first, bui Id a preliminary version of the meta-models after the initial simulated calculations; second, improve the accuracy and update the meta-models by adding some new representative samplings. By using this iterative strategy, the number of the initial sample points for real numerical simulations is greatly decreased and the time for the forged gear design is significantly shortened. Finally, an optimal design for an industrial application of a 27-teeth gear forging process was introduced, which includes three optimization variables and two objective functions. A 3D FE nu merical simulation model is used to realize the process and an advanced thermo-elasto-visco-plastic constitutive equation is considered to represent the material behavior. The meta-model applied for this example is kriging and the optimization algorithm is NSGA-II. At last, a relatively better Pareto optimal front (POF) is gotten with gradually improving the obtained surrogate meta-models.

  3. An efficient hybrid approach for multiobjective optimization of water distribution systems

    Science.gov (United States)

    Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

    2014-05-01

    An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

  4. Multi-objective optimization of aircraft design for emission and cost reductions

    Institute of Scientific and Technical Information of China (English)

    Wang Yu; Yin Hailian; Zhang Shuai; Yu Xiongqing

    2014-01-01

    Pollutant gases emitted from the civil jet are doing more and more harm to the environ-ment with the rapid development of the global commercial aviation transport. Low environmental impact has become a new requirement for aircraft design. In this paper, estimation method for emis-sion in aircraft conceptual design stage is improved based on the International Civil Aviation Orga-nization (ICAO) aircraft engine emissions databank and the polynomial curve fitting methods. The greenhouse gas emission (CO2 equivalent) per seat per kilometer is proposed to measure the emis-sions. An approximate sensitive analysis and a multi-objective optimization of aircraft design for tradeoff between greenhouse effect and direct operating cost (DOC) are performed with five geom-etry variables of wing configuration and two flight operational parameters. The results indicate that reducing the cruise altitude and Mach number may result in a decrease of the greenhouse effect but an increase of DOC. And the two flight operational parameters have more effects on the emissions than the wing configuration. The Pareto-optimal front shows that a decrease of 29.8%in DOC is attained at the expense of an increase of 10.8%in greenhouse gases.

  5. Parametric Design and Multiobjective Optimization of Maglev Actuators for Active Vibration Isolation System

    Directory of Open Access Journals (Sweden)

    Qianqian Wu

    2014-05-01

    Full Text Available The microvibration has a serious impact on science experiments on the space station and on image quality of high resolution satellites. As an important component of the active vibration isolation platform, the maglev actuator has a large stroke and exhibits excellent isolating performance benefiting from its noncontact characteristic. A maglev actuator with good linearity was designed in this paper. Fundamental features of the maglev actuator were obtained by finite element simulation. In order to minimize the coil weight and the heat dissipation of the maglev actuator, parametric design was carried out and multiobjective optimization based on the genetic algorithm was adopted. The optimized actuator has better mechanical properties than the initial one. Active vibration isolation platforms for different-scale payload were designed by changing the arrangement of the maglev actuators. The prototype to isolate vibration for small-scale payload was manufactured and the experiments for verifying the characteristics of the actuators were set up. The linearity of the actuator and the mechanical dynamic response of the vibration isolation platform were obtained. The experimental results highlight the effectiveness of the proposed design.

  6. Multi-objective optimization of aircraft design for emission and cost reductions

    Directory of Open Access Journals (Sweden)

    Wang Yu

    2014-02-01

    Full Text Available Pollutant gases emitted from the civil jet are doing more and more harm to the environment with the rapid development of the global commercial aviation transport. Low environmental impact has become a new requirement for aircraft design. In this paper, estimation method for emission in aircraft conceptual design stage is improved based on the International Civil Aviation Organization (ICAO aircraft engine emissions databank and the polynomial curve fitting methods. The greenhouse gas emission (CO2 equivalent per seat per kilometer is proposed to measure the emissions. An approximate sensitive analysis and a multi-objective optimization of aircraft design for tradeoff between greenhouse effect and direct operating cost (DOC are performed with five geometry variables of wing configuration and two flight operational parameters. The results indicate that reducing the cruise altitude and Mach number may result in a decrease of the greenhouse effect but an increase of DOC. And the two flight operational parameters have more effects on the emissions than the wing configuration. The Pareto-optimal front shows that a decrease of 29.8% in DOC is attained at the expense of an increase of 10.8% in greenhouse gases.

  7. Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework.

    Science.gov (United States)

    Yue, Dajun; Pandya, Shyama; You, Fengqi

    2016-02-02

    By combining life cycle assessment (LCA) with multiobjective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bioethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bioethanol production would be the most preferable choice from a climate perspective, while the mixed use of wheat and wheat straw as feedstocks would be the most cost-effective one.

  8. Optimal design of the front linkage of a hydraulic excavator for multi-objective function

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jong Won; Jung, Seung Min; Kim, Jong Won [Seoul National University, Seoul (Korea, Republic of); Kim, Jin Uk [Doosan Infracore, Incheon (Korea, Republic of); Seo, Tae Won [Yeungnam University, Gyeongsan (Korea, Republic of)

    2014-08-15

    The workspace, working velocity, excavating force, and load capacity of a hydraulic excavator play critical roles in the performance of the excavator for various tasks. This paper presents an optimal design of the front linkage of an excavator to maximize the performances of several indices simultaneously. A multi-objective function is defined to increase the excavator's workspace, working velocity, excavating force, and load capacity simultaneously. The workspace is defined by using four geometrical indices and the working velocity is defined by the amount of time needed to perform one cycle composed of digging and dumping. The excavating force consists of two forces, and the load capacity is defined by using the minimum values of three types with specific operations. A total of 10 indices define objective function with each weight, and pin-points of the front linkage are the design parameters, including joint positions of links and hydraulic actuators. A two-step optimization procedure is considered based on a new method called the hybrid Taguchi-random coordinate search algorithm. The results indicate a 3.43% increase in performance relative to the initial design parameters of a commercial excavator. More specifically, the excavator's workspace, working velocity, excavating force, and load capacity increase by 5.55%, 0.14%, 5.46%, and 0.33%, respectively. These improved design parameters can be applied to next generation excavators.

  9. Efficient Nondomination Level Update Method for Steady-State Evolutionary Multiobjective Optimization.

    Science.gov (United States)

    Li, Ke; Deb, Kalyanmoy; Zhang, Qingfu; Zhang, Qiang

    2016-11-08

    Nondominated sorting (NDS), which divides a population into several nondomination levels (NDLs), is a basic step in many evolutionary multiobjective optimization (EMO) algorithms. It has been widely studied in a generational evolution model, where the environmental selection is performed after generating a whole population of offspring. However, in a steady-state evolution model, where a population is updated right after the generation of a new candidate, the NDS can be extremely time consuming. This is especially severe when the number of objectives and population size become large. In this paper, we propose an efficient NDL update method to reduce the cost for maintaining the NDL structure in steady-state EMO. Instead of performing the NDS from scratch, our method only updates the NDLs of a limited number of solutions by extracting the knowledge from the current NDL structure. Notice that our NDL update method is performed twice at each iteration. One is after the reproduction, the other is after the environmental selection. Extensive experiments fully demonstrate that, comparing to the other five state-of-the-art NDS methods, our proposed method avoids a significant amount of unnecessary comparisons, not only in the synthetic data sets, but also in some real optimization scenarios. Last but not least, we find that our proposed method is also useful for the generational evolution model.

  10. Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Hongbo [Ritsumeikan Global Innovation Research Organization, Ritsumeikan University, 603-8577 Kyoto (Japan); Zhou, Weisheng; Nakagami, Ken' ichi [College of Policy Sciences, Ritsumeikan University, 603-8577 Kyoto (Japan); Gao, Weijun; Wu, Qiong [Faculty of Environmental Engineering, The University of Kitakyushu, 808-0135 Kitakyushu (Japan)

    2010-12-15

    Along with the continuing global warming, the environmental constraints are expected to play more and more important role in the operation of distributed energy resource (DER) systems, besides the economic objective. In this study, a multi-objective optimization model is developed to analyze the optimal operating strategy of a DER system while combining the minimization of energy cost with the minimization of environmental impact which is assessed in terms of CO{sub 2} emissions. The trade-off curve is obtained by using the compromise programming method. As an illustrative example, the DER system installed in an eco-campus in Japan has been selected for case study. The distributed technologies under consideration include photovoltaics (PV), fuel cell and gas engine for providing electrical and thermal demands. The obtained results demonstrate that increasing the satisfaction degree of economic objective leads to increased CO{sub 2} emissions. The operation of the DER system is more sensitive when environmental objective is paid more attention. Moreover, according to the sensitivity analysis, the consideration of electricity buy-back, carbon tax, as well as fuel switching to biogas, has more or less effect on the operation of DER systems. (author)

  11. A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem

    Directory of Open Access Journals (Sweden)

    Hao Yin

    2014-01-01

    Full Text Available For SLA-aware service composition problem (SSC, an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.

  12. A Multi-Objective Optimization of Cloud Based SLA-Violation Prediction and Adaptation

    Directory of Open Access Journals (Sweden)

    Vivek Gaur

    2016-06-01

    Full Text Available Monitoring of Cloud services is vital for both service providing organizations and consumers. The service providers need to maintain the quality of service to comply their services with the QoS parameters defined in SLA's such as response time, throughput, delay through continuous monitoring of services. The dynamic monitoring involves prediction of SLA violations and subsequent adaptation of the service compositions. The task of adaptation is in fact the task of discovering another plausible composition in the face of services recorded to have generated QoS violations. QoS- Driven Utility based service composition approach considers the individual user's priorities for QoS parameters and determines the overall utility measure of the service composition for the end user. In this work we present the problem of service composition adaptation as a multiobjective assignment optimization problem, which in turn is a NP-hard problem. The evolutionary algorithm GA with Tabu has been formulated as a Memetic and Pareto optimal approach for the adaptation problem and analyzed for efficiency in solving the problem

  13. A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Xiaohui Li

    2010-01-01

    Full Text Available A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific problem. Then, since this problem is NP hard in the strong sense, two well-known approximated methods, NSGA-II and SPEA-II, are adopted to solve it. Experimental results show the advantages of NSGA-II for the studied problem. An exact method is then applied to be compared with NSGA-II algorithm in order to prove the efficiency of the former. Experimental results show the advantages of NSGA-II for the studied problem. Computational experiments show that on all the tested instances, our NSGA-II algorithm was able to get the optimal solutions.

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

    Institute of Scientific and Technical Information of China (English)

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

    2008-01-01

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

  15. A multiobjective interval programming model for wind-hydrothermal power system dispatching using 2-step optimization algorithm.

    Science.gov (United States)

    Ren, Kun; Jihong, Qu

    2014-01-01

    Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision.

  16. A Multiobjective Interval Programming Model for Wind-Hydrothermal Power System Dispatching Using 2-Step Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Kun Ren

    2014-01-01

    Full Text Available Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision.

  17. Interior High Frequency Noise Analysis of Heavy Vehicle Cab and Multi-Objective Optimization with Statistical Energy Analysis Method

    Science.gov (United States)

    Chen, Shuming; Wang, Lianhui; Song, Jiqang; Wang, Dengfeng; Chen, Jing

    The interior sound pressure levels of a commercial vehicle cab at the driver’s right ear position and head rest position are determined as evaluation indices of vehicle acoustic performances. A statistical energy analysis model of the commercial vehicle cab was created by using statistical energy analysis method. The simulated interior acoustic performance of the cab has a significant coincidence with the experimental results. A response surface model was presented to determine the relationship between sound package parameters and evaluation indices of the interior acoustic performance for the vehicle cab. A multi-objective optimization was performed by using NSGA II algorithm with weighting coefficient method. The presented method provides a new idea for the multi-objective optimization design of the acoustic performances in vehicle noise analysis and control field.

  18. Local Approximation and Hierarchical Methods for Stochastic Optimization

    Science.gov (United States)

    Cheng, Bolong

    In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the optimal learning problem class, we introduce a locally linear model with radial basis function for estimating the posterior mean of the unknown objective function. The method uses a compact representation of the function which avoids storing the entire history, as is typically required by nonparametric methods. We derive a knowledge gradient policy with the locally parametric model, which maximizes the expected value of information. We show the policy is asymptotically optimal in theory, and experimental works suggests that the method can reliably find the optimal solution on a range of test functions. For the Markov decision processes problem class, we are motivated by an application where we want to co-optimize a battery for multiple revenue, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Computing the exact optimal policy becomes intractable due to the large state space and the number of time steps. We propose two methods to circumvent the computation bottleneck. First, we propose a nested MDP model that structure the co-optimization problem into smaller sub-problems with reduced state space. This new model allows us to understand how the battery behaves down to the two-second dynamics (that of the frequency regulation market). Second, we introduce a low-rank value function approximation for backward dynamic programming. This new method only requires computing the exact value function for a small subset of the state space and approximate the entire value function via low-rank matrix completion. We test these methods on historical price data from the

  19. The Optimality Conditions for Multiobjective Semi-infinite Programming Involving Generalized Unified (C, α, p, d)-convexity

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qing-xiang; ZHANG Yong-zhan

    2013-01-01

    The definition of generalized unified (C,α,p,d)-convex function is given.The concepts of generalized unified (C,α,p,d)-quasiconvexity,generalized unified (C,α,p,d)-pseudoconvexity and generalized unified (C,α,p,d)-strictly pseudoconvex functions are presented.The sufficient optimality conditions for multiobjective nonsmooth semi-infinite programming are obtained involving these generalized convexity lastly.

  20. Multi-Objective Optimization Design for Indirect Forced-Circulation Solar Water Heating System Using NSGA-II

    Directory of Open Access Journals (Sweden)

    Myeong Jin Ko

    2015-11-01

    Full Text Available In this study, the multi-objective optimization of an indirect forced-circulation solar water heating (SWH system was performed to obtain the optimal configuration that minimized the life cycle cost (LCC and maximized the life cycle net energy saving (LCES. An elitist non-dominated sorting genetic algorithm (NSGA-II was employed to obtain the Pareto optimal solutions of the multi-objective optimization. To incorporate the characteristics of practical SWH systems, operation-related decision variables as well as capacity-related decision variables were included. The proposed method was used to conduct a case study wherein the optimal configuration of the SWH system of an office building was determined. The case study results showed that the energy cost decreases linearly and the equipment cost increases more significantly as the LCES increases. However, the results also showed that it is difficult to identify the best solution among the Pareto optimal solutions using only the correlation between the corresponding objective function values. Furthermore, regression analysis showed that the energy and economic performances of the Pareto optimal solutions are significantly influenced by the ratio of the storage tank volume to the collector area (RVA. Therefore, it is necessary to simultaneously consider the trade-off and the effect of the RVA on the Pareto optimal solutions while selecting the best solution from among the optimal solutions.