An enhanced multi-objective evolutionary optimization algorithm with inverse model

被引：14

作者：

Zhang, Zhechen ^{[1
]}

Liu, Sanyang ^{[1
]}

Gao, Weifeng ^{[1
]}

Xu, Jingwei ^{[2
]}

Zhu, Shengqi ^{[2
]}

机构：

[1] Xidian Univ, Sch Math & Stat, Xian 710126, Peoples R China

[2] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710126, Peoples R China

来源：

INFORMATION SCIENCES | 2020年 / 530卷

基金：

中国国家自然科学基金;

关键词：

Multi-objective optimization; Estimation of distribution algorithms (EDAs); Adaptive reference vector; Inverse modelling; Gaussian processes (GPs); Nonrandom grouping; RM-MEDA; DECOMPOSITION; SELECTION;

D O I：

10.1016/j.ins.2020.03.111

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Multi-objective evolutionary algorithm based on the inverse model (IM-MOEA) is a popular method to solve multi-objective optimization problems (MOPs). However, IM-MOEA has some drawbacks such as low accuracy and difficulty in dealing with MOPs with irregular PFs. To address these issues, adaptive reference vector mechanism and nonrandom grouping strategy are employed in IM-MOEA, which enhances the reliability of the inverse model. In addition, a modified selection mechanism is used to choose candidate solutions. Further, an enhanced IM-MOEA with adaptive reference vectors and nonrandom grouping (AN-IMMOEA) is proposed in this paper. The experimental results on 27 MOPs indicate that the proposed method has a better performance than other MOEAs. (C) 2020 Elsevier Inc. All rights reserved.

引用

页码：128 / 147

页数：20

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