Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement

Evolutionary algorithms have been successfully applied for exploring both converged and diversified approximate Pareto-optimal fronts in multiobjective optimization problems, two- or three-objective in general. However, when solving problems with many objectives, nearly all algorithms perform poorly...

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Vydané v:IEEE transactions on evolutionary computation Ročník 20; číslo 1; s. 145 - 160
Hlavní autori: Zhenan He, Yen, Gary G.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Algorithms
Approximation
Convergence
Design improvements
Deterioration
diversity improvement strategy
Diversity methods
Evolutionary algorithms
Evolutionary computation
many-objective evolutionary algorithms
many-objective optimization problems
Objective space reduction
Optimization
Sociology
Statistics
Strategy
many-objective evolutionary algorithms (EAs)
many-objective optimization problems (MaOPs)
Diversity improvement strategy
objective space reduction
ISSN:1089-778X, 1941-0026
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Shrnutí:Evolutionary algorithms have been successfully applied for exploring both converged and diversified approximate Pareto-optimal fronts in multiobjective optimization problems, two- or three-objective in general. However, when solving problems with many objectives, nearly all algorithms perform poorly due to the loss of selection pressure in fitness evaluation. An extremely large objective space could inadvertently deteriorate the effect of an evolutionary operator. In this paper, we propose a new approach to directly handle the challenges to solve many-objective optimization problems (MaOPs). This novel design includes two stages: first, the whole population quickly approaches a small number of "target" points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and well distribute. As a case study, the proposed algorithm based on this design is compared with five state-of-the-art algorithms. Experimental results show that the proposed method exhibits improved performance in both convergence and diversity for solving MaOPs.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2433266