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

Kata PRADITWONG, Xin YAO

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114 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. ©2006 IEEE.
Original languageEnglish
Title of host publication2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
PublisherIEEE Computer Society
Pages286-291
Number of pages6
Volume1
ISBN (Print)9781424406050
DOIs
Publication statusPublished - Nov 2006
Externally publishedYes

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