Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms

Evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general. However, when problems with many objectives are encountered, nearly all algorithms perform poorly due to loss of selection pressure in fitness...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 18; H. 2; S. 269 - 285
Hauptverfasser:	He, Zhenan, Yen, Gary G., Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.04.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Benchmark testing Computer science; control theory; systems Convergence Evolutionary algorithms Evolutionary computation Exact sciences and technology Fitness Fuzzy Fuzzy logic Fuzzy set theory Fuzzy sets multiobjective evolutionary algorithm NSGA-II Object recognition Optimization Pareto optimality Pareto optimization Search problems SPEA2 Theoretical computing Vectors NSGA-II Pareto optimum Evolutionary algorithm Multiobjective programming Dominance SPEA2 multiobjective evolutionary algorithm Fuzzy logic Experimental result Genetic algorithm Classification Fuzzy relation Pareto optimality Optimality principle Mathematical programming
ISSN:	1089-778X, 1941-0026
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Abstract	Evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general. However, when problems with many objectives are encountered, nearly all algorithms perform poorly due to loss of selection pressure in fitness evaluation solely based upon the Pareto optimality principle. In this paper, we introduce a new fitness evaluation mechanism to continuously differentiate individuals into different degrees of optimality beyond the classification of the original Pareto dominance. The concept of fuzzy logic is adopted to define a fuzzy Pareto domination relation. As a case study, the fuzzy concept is incorporated into the designs of NSGA-II and SPEA2. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the original ones for solving many-objective optimization problems.
AbstractList	Evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general. However, when problems with many objectives are encountered, nearly all algorithms perform poorly due to loss of selection pressure in fitness evaluation solely based upon the Pareto optimality principle. In this paper, we introduce a new fitness evaluation mechanism to continuously differentiate individuals into different degrees of optimality beyond the classification of the original Pareto dominance. The concept of fuzzy logic is adopted to define a fuzzy Pareto domination relation. As a case study, the fuzzy concept is incorporated into the designs of NSGA-II and SPEA2. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the original ones for solving many-objective optimization problems.
Author	He, Zhenan Yen, Gary G. Zhang, Jun
Author_xml	– sequence: 1 givenname: Zhenan surname: He fullname: He, Zhenan email: gyen@okstate.edu organization: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA – sequence: 2 givenname: Gary G. surname: Yen fullname: Yen, Gary G. email: zhenan@okstate.edu organization: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA – sequence: 3 givenname: Jun surname: Zhang fullname: Zhang, Jun email: junzhanghk@mail.sysu.edu.cn organization: Department of Computer Science, Sun Yat-Sen University, Guangzhou, China
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Keywords	NSGA-II Pareto optimum Evolutionary algorithm Multiobjective programming Dominance SPEA2 multiobjective evolutionary algorithm Fuzzy logic Experimental result Genetic algorithm Classification Fuzzy relation Pareto optimality Optimality principle Mathematical programming
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Snippet	Evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general....
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SubjectTerms	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Benchmark testing Computer science; control theory; systems Convergence Evolutionary algorithms Evolutionary computation Exact sciences and technology Fitness Fuzzy Fuzzy logic Fuzzy set theory Fuzzy sets multiobjective evolutionary algorithm NSGA-II Object recognition Optimization Pareto optimality Pareto optimization Search problems SPEA2 Theoretical computing Vectors
Title	Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms
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