A Short Survey and Challenges for Multiobjective Evolutionary Algorithms Based on Decomposition

Abstract

Decomposition methods and evolution mechanisms enable multiobjective evolutionary algorithms based on decomposition (MOEA/D) to tackle many complex optimization problems efficiently. Therefore, it has found wide application in various fields and attracted significant attention of researchers around the world since it was first proposed by Zhang and Li in 2007. This paper presents a review of the basic idea of MOEA/D and a survey of its major variants, including the improvement of key components such as decomposition method, weight vector generation method and evolutionary operator, and the extension to many-objective optimization. In addition, some challenges and potential research directions of its theoretical and application research are discussed.