A Novel Hybrid Multi-Objective Population Migration Algorithm

This paper presents a multi-objective co-evolutionary population migration algorithm based on Good Point Set (GPSMCPMA) for multi-objective optimization problems (MOP) in view of the characteristics of MOPs. The algorithm introduces the theory of good point set (GPS) and dynamic mutation operator (DMO) and adopts the entire population co-evolutionary migration, based on the concept of Pareto nondomination and global best experience and guidance. The performance of the algorithm is tested through standard multi-objective functions. The experimental results show that the proposed algorithm performs much better in the convergence, diversity and solution distribution than SPEA2, NSGA-II, MOPSO and MOMASEA. It is a fast and robust multi-objective evolutionary algorithm (MOEA) and is applicable to other MOPs.