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

被引:19
作者
Li, Wei [1 ]
Gong, Wenyin [1 ]
Ming, Fei [1 ]
Wang, Ling [2 ]
机构
[1] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China
[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
关键词
Constrained multi-objective optimization; Evolutionary algorithm; Two archive; Fitness evaluation; Mating selection; OPTIMIZATION; DECOMPOSITION; PERFORMANCE;
D O I
10.1016/j.knosys.2022.108732
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:8
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