Research progress of constrained multi-objective evolutionary algorithms

Zhu Yawen
Zhou Hongbiao
Li Yang
Xu Haoyuan
Faculty of Automation, Huaiyin Institute of Technology, Huai'an Jiangsu 223003, China

Abstract

CMOEAs can deal with multiple conflicting objective functions and constraints simultaneously and guide the population towards the optimal solution in the feasible domain, which has received extensive attention from researchers. This paper firstly introduced the relevant definitions of constrained multi-objective optimization problems(CMOPs) and three classifications of multi-objective evolutionary algorithms(MOEAs). Secondly, it systematically analyzed the current constraint processing mechanisms in CMOEAs, and condensed the current four main constraint processing methods. Then, it reviewed the research progress of CMOEAs in detail from three aspects: dominance-based, index-based and decomposition-based. Finally, it pointed out the challenges and future research directions of CMOEAs.

Foundation Support

江苏省产学研合作项目(BY2020247)
江苏省研究生科研实践创新计划资助项目(SJCX21_1509)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0041
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 9
Section: Survey
Pages: 2582-2590,2602
Serial Number: 1001-3695(2022)09-003-2582-09

Publish History

[2022-04-15] Accepted Paper
[2022-09-05] Printed Article

Cite This Article

朱亚文, 周红标, 李杨, 等. 约束多目标进化算法研究进展 [J]. 计算机应用研究, 2022, 39 (9): 2582-2590,2602. (Zhu Yawen, Zhou Hongbiao, Li Yang, et al. Research progress of constrained multi-objective evolutionary algorithms [J]. Application Research of Computers, 2022, 39 (9): 2582-2590,2602. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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