Constrained multi-objective evolutionary algorithm with an improved two-archive strategy

Solving constrained multi-objective optimization problems (CMOPs) obtains considerable attention in the evolutionary computation community. Various constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for the CMOPs in the last few decades. Among the CMOEA techniques, two...

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Vydáno v:Knowledge-based systems Ročník 246; s. 108732
Hlavní autoři: Li, Wei, Gong, Wenyin, Ming, Fei, Wang, Ling
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 21.06.2022
Elsevier Science Ltd
Témata:
Algorithms
Archives & records
Computation
Constrained multi-objective optimization
Convergence
Evaluation
Evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Experiments
Fitness
Fitness evaluation
Genetic algorithms
Mating
Mating selection
Multiple objective analysis
Objectives
Optimization
Strategies
Strategy
Two archive
Constrained multi-objective optimization
Fitness evaluation
Mating selection
Evolutionary algorithm
Two archive
ISSN:0950-7051, 1872-7409
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Shrnutí:Solving constrained multi-objective optimization problems (CMOPs) obtains considerable attention in the evolutionary computation community. Various constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for the CMOPs in the last few decades. Among the CMOEA techniques, two archive strategy is an effective approach, and enhancing the performance of C-TAEA based on two archive framework is a promising direction. This paper proposes an improved two-archive-based evolutionary algorithm, referred to as C-TAEA2. In C-TAEA2, a new fitness evaluation strategy for the convergence archive (CA) is presented to achieve better convergence. Additionally, a fitness evaluation method is proposed to evaluate solutions of the diversity archive (DA) to further promote diversity. Moreover, new update strategies are designed for both CA and DA to reduce the computational cost. Based on the new fitness evaluation strategies, a new mating selection strategy is also developed. Experiments on different benchmark CMOPs demonstrate that C-TAEA2 obtained better or highly competitive performance compared to other state-of-the-art CMOEAs. •An improved two-archive based EA is proposed for the CMOPs.•New fitness evaluation strategies are designed for the two archives.•An enhanced mating selection strategy is developed.•Results demonstrate the superiority of our approach to other CMOEAs.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.108732