Multi-Objective Differential Evolution based on the Summation of Normalized Objectives and Improved Selection Method


In this paper, an improved selection method is proposed and integrated with summation of normalized objectives based multi-objective differential evolution to solve multi-objective optimization problems. The summation of normalized objectives and diversified selection is used to replace the non-domination sorting and reduce the simulation time of the multi-objective evolutionary algorithm. However, the diversified selection may keep some bad individuals as parents which lead to poor performance. With the proposed method, a pre-selection is applied to filter the bad solutions and improve the convergence. The algorithm is tested on 15 commonly used benchmark problems and compared with a number of multi-objective evolutionary algorithms in literature. The results show that the proposed algorithm is effective and efficient.