A decomposition based multiobjective evolutionary algorithm with classification

Xi Lin, Qingfu Zhang, Sam Kwong

Abstract: This paper investigates how to use a pre-selection approach to improve the performance of the multiobjective evolutionary algorithm based on decomposition (MOEA/D). It proposes a novel MOEA/D algorithm with classification to serve this purpose. The proposed algorithm builds a classification model on the search space to filter all new generated solutions, and mainly evaluates those promising solutions for reducing real function evaluation costs during the search process. Experimental study on different test instances validates that the pre-selection approach can significantly improve the performance of a classical MOEA/D.