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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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 18; no. 2; pp. 269 - 285
Main Authors:	He, Zhenan, Yen, Gary G., Zhang, Jun
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.04.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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