Adaptive differential evolution for multi-objective optimization

Zai WANG, Zhenyu YANG, Ke TANG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

No existing multi-objective evolutionary algorithms (MO-EAs) have ever been applied to problems with more than 1000 real-valued decision variables. Yet the real world is full of large and complex multi-objective problems. Motivated by the recent success of SaNSDE [1], an adaptive differential evolution algorithm that is capable of dealing with more than 1000 real-valued decision variables effectively and efficiently, this paper extends the ideas behind SaNSDE to develop a novel MOEA named MOSaNSDE. Our preliminary experimental studies have shown that MOSaNSDE outperforms state-of-the-art MOEAs significantly on most problems we have tested, in terms of both convergence and diversity metrics. Such encouraging results call for a more in-depth study of MOSaNSDE in the future, especially about its scalability. © 2009 Springer Berlin Heidelberg.
Original languageEnglish
Title of host publicationCutting-Edge Research Topics on Multiple Criteria Decision Making : 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, China, June 21-26, 2009. Proceedings
EditorsYong SHI, Shouyang WANG, Yi PENG, Jianping LI, Yong ZENG
PublisherSpringer Berlin Heidelberg
Pages9-16
Number of pages8
ISBN (Electronic)9783642022982
ISBN (Print)9783642022975
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event20th International Conference on Multiple Criteria Decision Makin, MCDM 2009 - Chengdu, China
Duration: 21 Jun 200926 Jun 2009

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Berlin, Heidelberg
Volume35
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference20th International Conference on Multiple Criteria Decision Makin, MCDM 2009
Country/TerritoryChina
CityChengdu
Period21/06/0926/06/09

Bibliographical note

This work is partially supported by the National Natural Science Foundation of China (Grant No. 60428202), The Fund for Foreign Scholars in University Research and Teaching Programs (Grant No. B07033) and an EPSRC Grant (EP/D052785/1) on “SEBASE: Software Engineering By Automated SEarch”.

Keywords

  • Particle Swarm Optimization
  • Multiobjective Optimization
  • Nondominated Solution
  • External Archive
  • Pareto Archive Evolution Strategy

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