A comparative study on decomposition-based multi-objective evolutionary algorithms for many-objective optimization

Xiaoliang Ma, Junshan Yang, Nuosi Wu, Zhen Ji, Zexuan Zhu

Abstract: Many-objective optimization problems pose challenges to the Pareto-based multi-objective optimization algorithms. Recent studies have suggested that decomposition is a promising method to improve the performance of multi-objective evolutionary algorithms on many-objective optimization problem. Various methods based on decomposition have been developed to solve many-objective problems in recent years. However, the existing experimental comparative studies are usually limited to only a few methods based on decomposition. This paper offers a systematic comparison of seven representative decomposition-based approaches tested on two groups of widely used problems. The experimental results have demonstrated that none of the compared algorithms has a clear advantage over the others, although different algorithms are competitive on different test problems. Therefore, a careful selection of algorithms is necessary in handling a many-objective problem in hand.