A new two-stage based evolutionary algorithm for solving multi-objective optimization problems

被引：14

作者：

Wang, Yiming ^{[1
]}

Gao, Weifeng ^{[1
]}

Gong, Maoguo ^{[2
]}

Li, Hong ^{[1
]}

Xie, Jin ^{[1
]}

机构：

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

[2] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Int Res Ctr Intelligent Percept & Computat, Xian 710071, Peoples R China

来源：

INFORMATION SCIENCES | 2022年 / 611卷

关键词：

Multi -objective optimization; Constrained sub -problem; Evolutionary algorithm; DOMINANCE RELATION; MOEA/D;

D O I：

10.1016/j.ins.2022.07.180

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

It is a challenge to balance the convergence and the diversity in multi-objective optimization problems. In this paper, a new two-stage based evolutionary algorithm (MOEA/TS) is proposed, where the convergence and the diversity are handled in two inde-pendent phases. In the first stage, the convergence is accelerated by using the gradient information of constrained sub-problems. In the second stage, the diversity is improved by adopting the dominance based multi-objective evolutionary algorithm. The compara-tive experiments are presented in terms of two performance indicators for benchmark test problems. The results indicates that MOEA/TS has the competitive performance. (c) 2022 Elsevier Inc. All rights reserved.

引用

页码：649 / 659

页数：11

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