Evolutionary Multimodal Optimization Based on Bi-Population and Multi-Mutation Differential Evolution
Wei Li, Yaochi Fan, Qingzheng Xu · International Journal of Computational Intelligence Systems · 2020
The most critical issue of multimodal evolutionary algorithms (EAs) is to find multiple distinct global optimal solutions in a run.EAs have been considered as suitable tools for multimodal optimization because of their population-based structure.However, EAs tend to converge toward one of the optimal solutions due to the difficulty of population diversity preservation.In this paper, we propose a bi-population and multi-mutation differential evolution (BMDE) algorithm for multimodal optimization problems.The novelties and contribution of BMDE include the following three aspects: First, bi-population evolution strategy is employed to perform multimodal optimization in parallel.The difference between inferior solutions and the current population can be considered as a promising direction toward the optimum.Second, multi-mutation strategy is introduced to balance exploration and exploitation in generating offspring.Third, the update strategy is applied to individuals with high similarity, which can improve the population diversity.Experimental results on CEC2013 benchmark problems show that the proposed BMDE algorithm is better than or at least comparable to the state-of-the-art multimodal algorithms in terms of the quantity and quality of the optimal solutions.