A Niching-Based Reproduction and Preselection-Based Multiobjective Differential Evolution for Multimodal Multiobjective Optimization

Hongyu Lin, Jing J. Liang, Caitong Yue, Yaonan Wang

Abstract: In multimodal multiobjective optimization problems (MMOPs), there are several Pareto optimal solutions corresponding to the identical objective vector. MMOPs pose greater challenges for multiobjective optimization evolutionary algorithms (MOEAs) as they require balancing the convergence and diversity of the population in both the decision space and the objective space. Therefore, this paper proposes a novel coevolutionary multiobjective optimization differential evolution algorithm with a niching-based reproduction and a preselection-based environmental selection mechanism, called NPCMODE. The algorithm introduces a niching-based reproduction, evolving solutions independently within multiple niches to generate more dispersed solutions in the decision space. Additionally, a preselection-based environmental selection mechanism priori-tizes solutions with low density in both decision and objective spaces through a dual-population coevolutionary framework. The efficiency of NPCMODE is validated through comparative experiments with six representative multimodal multiobjective optimization evolutionary algorithms (MMOEAs) on the CEC 2019 benchmark suite, showcasing its effectiveness in achieving a balance between convergence and diversity performance.