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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Vydáno v:IEEE transactions on evolutionary computation Ročník 18; číslo 2; s. 269 - 285
Hlavní autoři: He, Zhenan, Yen, Gary G., Zhang, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.04.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2013.2258025