On the Performance of Metamodel Assisted MOEA/D

Abstract. MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilizes 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 multiobjective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analyzed and compared on several test problems, which shows a promising perspective on this method.

• Type: Conference
• Year: 2007
• Where: ISICA
• Authors: Wudong Liu, Qingfu Zhang, Edward P. K. Tsang, Cao Liu, Botond Virginas
• Keywords: Black-box Multiobjective Problems, ISICA 2007, Nowadays Mainstream Moea, Test Problems