On the performance of metamodel assisted MOEA/D

Authors:
Wudong Liu;Qingfu Zhang;Edward Tsang;Cao Liu;Botond Virginas
Affiliations:
Department of Computer Science, University of Essex, UK;Department of Computer Science, University of Essex, UK;Department of Computer Science, University of Essex, UK;Faculty of Computer Science, China University of Geosciences, China;BT Research Laboratories, UK
Venue:
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Year:
2007

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Abstract

MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilises the idea of problem decomposition to tackle the complexity from multiple objectives. It shows better performance than most nowadays mainstream MOEA methods in various test problems, especially on the quality of solution's distribution in the Pareto set. This paper aims to bring the strength of metamodel into MOEA/D to help the solving of expensive black-box multi-objective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analysed and compared on several test problems, which shows a promising perspective on this method.