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6th World Congress on Intelligent Control and Automation

作者： Ma, Qingliang Hu, Changhua Xian Res Inst Hi Tech Teaching & Res Unit 302 Xian 710025 Shaanxi Peoples R China

ISBN: (纸本)1424403316

A novel approach to mixed H-2/H-infinity filtering with regional pole assignment is proposed based on multiobjective evolutionary algorithm (MOEA). To design optimal H-2/H-infinity filters with less conservatism, a kind of hybrid MOEA (HMOEA) is especially developed. Firstly, evolutionary search is performed by HMOEA to obtain a set of approximate Pareto optimal solutions. Then, a local search is carried out with each previously obtained solution to design better H-2/H-infinity filters. An illustrative example is given to demonstrate the effectiveness and advantages of the proposed method.

关键词： multiobjective filtering problem regional pole assignment multiobjective evolutionary algorithms

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Improving Many-Objective Optimization Performance by Sequencing evolutionary algorithms 14

Improving Many-Objective Optimization Performance by Sequenc...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Dohr, Martin Eichberger, Bernd Graz Univ Technol Inst Elect A-8010 Graz Austria

ISBN: (纸本)9781450326629

evolutionary multiobjective optimization (EMO) has been successfully applied to various real-world scenarios with usually two or three contradicting optimization goals. However, several studies have pointed out a great deterioration of computational performance when handling more than three objectives. In order to improve the scalability of multiobjective evolutionary algorithms (MOEAs) onto higher-dimensional objective spaces, techniques using e.g. scalarizing functions and preference-or indicator-based guidance have been proposed. Most of those proposals require a-priori information or a decision maker during optimization, which increases the complexity of the algorithms. In this paper, we propose a divide and conquer method for many-objective optimization. First, we partition a problem into lower-dimensional subproblems for which standard algorithms are known to perform very well. Our key improvement is the sequential usage of MOEAs, utilizing the results of one suboptimization as initial population for another MOEA. This technique allows modular optimization phases and can be applied to common evolutionary algorithms. We test our enhanced method on the hard to solve multiobjective Quadratic Assignment Problem (mQAP), using a variety of established MOEAs.

关键词： algorithms Performance Experimentation multiobjective evolutionary algorithms many-objective optimization multiobjective quadratic assignment problem

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Exploiting a New Interpretability Index in the Multi-Objective evolutionary Learning of Mamdani Fuzzy Rule-based Systems

Exploiting a New Interpretability Index in the Multi-Objecti...

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9th International Conference on Intelligent Systems Design and Applications

作者： Antonelli, Michela Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Pisa Italy

ISBN: (纸本)9781424447350

In this paper, we introduce a new index for evaluating the interpretability of Mamdani fuzzy rule-based systems (MFRBSs). The index takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in the multi-objective evolutionary generation of MFRBSs with different trade-offs between accuracy and interpretability. The rule base and the membership function parameters of the MFRBSs are learnt concurrently by exploiting an appropriate chromosome coding and purposely-defined genetic operators. Results on a real-world regression problem are shown and discussed.

关键词： Mamdani Fuzzy Rule-based Systems multiobjective evolutionary algorithms Interpretability Index Accuracy-Interpretability trade-off Piecewise Linear Transformation

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Many-Objective Optimization using Differential Evolution with Variable-Wise Mutation Restriction 13

Many-Objective Optimization using Differential Evolution wit...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Denysiuk, Roman Costa, Lino Santo, Isabel Espirito Univ Minho Algoritmi R&D Ctr Campus Gualtar P-4710057 Braga Portugal Univ Minho Dept Prod & Syst Engn P-4710057 Braga Portugal

ISBN: (纸本)9781450319638

In this paper, we propose an evolutionary algorithm for handling many-objective optimization problems called MyO-DEMR (many-objective differential evolution with mutation restriction). The algorithm uses the concept of Pareto dominance coupled with the inverted generational distance metric to select the population of the next generation from the combined multi-set of parents and offspring. Furthermore, we suggest a strategy for the restriction of the difference vector in DE operator in order to improve the convergence property in multi-modal fitness landscape. We compare MyO-DEMR with other state-of-the-art multiobjective evolutionary algorithms on a number of multiobjective optimization problems having up to 20 dimensions. The results reveal that the proposed selection scheme is able to effectively guide the search in high-dimensional objective space. Moreover, MyO-DEMR demonstrates significantly superior performance on multi-modal problems comparing with other DE-based approaches.

关键词： multiobjective optimization multiobjective evolutionary algorithms performance assessment

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Design of a motorcycle frame using neuroacceleration strategies in MOEAs

Design of a motorcycle frame using neuroacceleration strateg...

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13th Biennial Latin-Ibero-American Conference on Operations Research

作者： Rodriguez, Jorge E. Medaglia, Andres L. Coello Coello, Carlos A. Univ Los Andes Dept Ingn Ind Ctr Optimizac & Probabilidad Aplicada Bogota Colombia CINVESTAV IPN Dept Comp Mexico City 07300 DF Mexico

Designing a low-budget lightweight motorcycle frame with superior dynamic and mechanical properties is a complex engineering problem. This complexity is due in part to the presence of multiple design objectives-mass, structural stress and rigidity-, the high computational cost of the finite element (FE) simulations used to evaluate the objectives, and the nature of the design variables in the frame's geometry (discrete and continuous). Therefore, this paper presents a neuroacceleration strategy for multiobjective evolutionary algorithms (MOEAs) based on the combined use of real (FE simulations) and approximate fitness function evaluations. The proposed approach accelerates convergence to the Pareto optimal front (POF) comprised of nondominated frame designs. The proposed MOEA uses a mixed genotype to encode discrete and continuous design variables, and a set of genetic operators applied according to the type of variable. The results show that the proposed neuro-accelerated MOEAs, NN-NSGA II and NN-MicroGA, improve upon the performance of their original counterparts, NSGA II and MicroGA. Thus, this neuroacceleration strategy is shown to be effective and probably applicable to other FE-based engineering design problems.

关键词： multiobjective evolutionary algorithms Finite element analysis Neural networks Motorcycle Engineering design multiobjective optimization

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Hydrodynamic Design of Control Surfaces for Ships Using a MOEA with Neuronal Correction

Hydrodynamic Design of Control Surfaces for Ships Using a MO...

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4th International Workshop on Hybrid Artificial Intelligence Systems

作者： Diaz-Casas, V. Bellas, Francisco Lopez-Pena, Fernando Duro, Richard Univ A Coruna Integrated Grp Engn Res Ferrol 15403 Spain

ISBN: (纸本)9783642023187

In this paper we present a hybrid intelligent system for the hydrodynamic design of control surfaces on ships. Our main contribution here is the hybridization of multiobjective evolutionary algorithms (MOEA) and a neural correction procedure in the fitness evaluation stage that permits obtaining solutions that are precise enough for the MOEA to operate with, while drastically reducing the computational cost of the simulation stage for each individual. The MOEA searches for the optimal solutions and the neuronal system corrects the deviations of the simplified simulation model to obtain a more realistic design. This way, we can exploit the benefits of a MOEA decreasing the computational cost in the evaluation of the candidate solutions while preesrving the reliability of the simulation model. The proposed hybrid system is successfully applied in the design of a 2D control surface for ships and extended to a 3D one.

关键词： Automatic Design multiobjective evolutionary algorithms Artificial Neural Networks Control Surfaces

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A novel strategy to preserve diversity in solving MOEAs based on artificial emphasis on uniformity

A novel strategy to preserve diversity in solving MOEAs base...

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IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS)

作者： Wang, Jiasen Li, Huiyuan Zheng, Zejiu Department of Automation Wuhan University of Technology Wuhan China School of Mechanical Engineering Shanghai Dianji University Shanghai China

ISBN: (纸本)9781424465835

Diversity distribution plays a significant role in assessing performances of MOEAs, so we proposed LRCD (Left-Right Crowding Distance) in [5] to get a better performance than that of the original NSGA2. There are two aspects of diversity, they are span and uniformity. Based on LRCD, we will propose W-LRCD and E-LRCD to put artificial emphasis on uniformity in calculation of crowding distance in this paper. Six Multi-Objective test problems have been done to test the two methods' performances, we find E-LRCD performs the best, LRCD the second, W-LRCD the third and the original NSGA2 ranked the last on diversity distribution in the six test problems. ©2010 IEEE.

关键词： Left-Right Crowding Distance MOEAs NSGA2 artificial emphasis diversity distribution diversity preservation evolutionary computation left-right crowding distance maintenance engineering multiobjective evolutionary algorithms multiobjective test problems statistical analysis

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evolutionary multiobjective Optimization in Materials Science and Engineering

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MATERIALS AND MANUFACTURING PROCESSES 2009年第2期24卷 119-129页

作者： Coello Coello, Carlos A. Landa Becerra, Ricardo IPN CINVESTAV Dept Comp Evolutionary Computat Grp Mexico City 07360 DF Mexico

This article provides a short introduction to the evolutionary multiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary algorithms that have been developed, from a historical perspective. In the second part of the article, some representative applications within materials science and engineering are reviewed. In the final part of the article, some potential areas for future research in this area are briefly described.

关键词： Alloys design Applications of evolutionary algorithms Casting of metals Cold rolling evolutionary algorithms evolutionary multiobjective optimization Heat treatment Hot rolling Iron-making Metaheuristics multiobjective evolutionary algorithms multiobjective optimization Polymerization Reheating Steel-making

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Discovery of Implicit Objectives by Compression of Interaction Matrix in Test-Based Problems

Discovery of Implicit Objectives by Compression of Interacti...

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13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Liskowski, Pawel Krawiec, Krzysztof Poznan Univ Tech Inst Comp Sci Poznan Poland

ISBN: (纸本)9783319107622;9783319107615

In test-based problems, commonly solved with competitive coevolution algorithms, candidate solutions (e.g., game strategies) are evaluated by interacting with tests (e.g., opponents). As the number of tests is typically large, it is expensive to calculate the exact value of objective function, and one has to elicit a useful training signal (search gradient) from the outcomes of a limited number of interactions between these coevolving entities. Averaging of interaction outcomes, typically used to that aim, ignores the fact that solutions often have to master different and unrelated skills, which form underlying objectives of the problem. We propose a method for on-line discovery of such objectives via heuristic compression of interaction outcomes. The compressed matrix implicitly defines derived search objectives that can be used by traditional multiobjective search techniques (NSGA-II in this study). When applied to the challenging variant of multi-choice Iterated Prisoner's Dilemma problem, the proposed approach outperforms conventional two-population coevolution in a statistically significant way.

关键词： Test-based problems coevolution iterated prisoner dilemma multiobjective evolutionary algorithms

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Distributed NSGA-II with Migration using Compensation on Many-core Processors for Improving Performance and Accuracy 17

Distributed NSGA-II with Migration using Compensation on Man...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Sato, Yuji Sato, Mikiko Miyakawa, Minami Hosei Univ 3-7-2 Kajinocho Koganei Tokyo 1848584 Japan Tokai Univ Minato Ku 2-3-23 Takanawa Tokyo 1088619 Japan

ISBN: (纸本)9781450349390

A recent trend in multiobjective evolutionary algorithms is to increase the population size to approximate the nondominated solution set with high accuracy. And the execution time becomes a problem in engineering applications. In this paper, we propose distributed, high-speed NSGA-II using a many-core environment to obtain a Pareto-optimal solution set excelling in convergence and diversity. This method improves performance while maintaining the accuracy of the Pareto-optimal solution set by repeating NSGA-II distributed processing in a many-core environment inspired by the divide-and-conquer method together with migration processing for compensation of the nondominated solution set obtained by distributed processing. On comparing with NSGA-II executing on a single CPU and parallel, high-speed NSGA-II using a standard island model, it was found that the proposed method greatly shortened the execution time for obtaining a Pareto-optimal solution set with equivalent hypervolume while increasing the accuracy of solution searching.

关键词： multiobjective evolutionary algorithms NSGA-II distributed processing many-core environment

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