A replacement strategy for balancing convergence and diversity in MOEA/D

Zhenkun Wang*, Qingfu Zhang, Maoguo Gong, Aimin Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

49 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2132-2139
Number of pages8
ISBN (Electronic)9781479914883
DOIs
StatePublished - 16 Sep 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

Keywords

  • MOEA/D
  • Multiobjective optimization
  • replacement
  • selection operator

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