A Voronoi Neighborhood Based Differential Evolution Algorithm for Multimodal Multi-objective Optimization

Abstract

This paper proposes a parameter-free Voronoi neighborhood based differential evolution (MMODE-VN) to solve the multimodal multi-objective optimization problems. First, the Voronoi neighborhood concept without a prior knowledge is employed to form niches in the population. Meanwhile, the leaders of matching neighborhood are used to generate variation vector with a novel elite learning strategy, which enhances global search ability. The comparison experiments between MMODE-VN and five multimodal multi-objective optimization algorithms on CEC 2019 MMOPs test suite have been conducted. The experimental results show that the performance of the proposed method is better than the comparison algorithms.