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  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.
|Title of host publication
|2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
|IEEE Computer Society
|Number of pages
|Published - Nov 2006