A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm

Kata PRADITWONG, Xin YAO

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

7 Citations (Scopus)

Abstract

Many Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed in recent years. However, almost all MOEAs have been evaluated on problems with two to four objectives only. It is unclear how well these MOEAs will perform on problems with a large number of objectives. Our preliminary study [1] showed that performance of some MOEAs deteriorates significantly as the number of objectives increases. This paper proposes a new MOEA that performs well on problems with a large number of objectives. The new algorithm separates non-dominated solutions into two archives, and is thus called the Two-Archive algorithm. The two archives focused on convergence and diversity, respectively, in optimisation. Computational studies have been carried out to evaluate and compare our new algorithm against the best MOEA for problems with a large number of objectives. Our experimental results have shown that the Two-Archive algorithm outperforms existing MOEAs on problems with a large number of objectives. © Springer-Verlag Berlin Heidelberg 2007.
Original languageEnglish
Title of host publicationComputational Intelligence and Security : International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers
EditorsYuping WANG, Yiu-ming CHEUNG, Hailin LIU
PublisherSpringer Berlin Heidelberg
Pages95-104
Number of pages10
ISBN (Electronic)9783540743774
ISBN (Print)9783540743767
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2006 International Conference on Computational and Information Science, CIS 2006 - Guangzhou, China
Duration: 3 Nov 20066 Nov 2006

Conference

Conference2006 International Conference on Computational and Information Science, CIS 2006
Country/TerritoryChina
CityGuangzhou
Period3/11/066/11/06

Keywords

  • Pareto Front
  • Multiobjective Optimization
  • Total Size
  • Strength Pareto Evolutionary Algorithm
  • Removal Strategy

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