A Replacement Strategy for Balancing Convergence and Diversity in MOEA/D

被引：0

作者：

Wang, Zhenkun ^{[1
]}

Zhang, Qingfu ^{[2
,3
]}

Gong, Maoguo ^{[1
]}

Zhou, Aimin ^{[4
]}

机构：

[1] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Shaanxi Provinc, Peoples R China

[2] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China

[3] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England

[4] E China Normal Univ, Dept Comp Sci & Technol, Shanghai 200241, Peoples R China

来源：

2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2014年

关键词：

Multiobjective optimization; MOEA/D; selection operator; replacement; MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS; PERFORMANCE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies the replacement schemes in MOEA/D and proposes a new replacement named global replacement. It can improve the performance of MOEA/D. Moreover, trade-offs between convergence and diversity can be easily controlled in this replacement strategy. It also shows that different problems need different trade-offs between convergence and diversity. We test the MOEA/D with this global replacement on three sets of benchmark problems to demonstrate its effectiveness.

引用

页码：2132 / 2139

页数：8

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