A New Multi-objective Evolutionary Optimisation Algorithm: The Two-Archive Algorithm

Authors:
Kata Praditwong;Xin Yao
Affiliations:
The Centre of Excellence for Research in Computational Intelligence and Applications(CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;The Centre of Excellence for Research in Computational Intelligence and Applications(CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Venue:
Computational Intelligence and Security
Year:
2007

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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-Archivealgorithm. 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-Archivealgorithm outperforms existing MOEAs on problems with a large number of objectives.