An Adaptive Resource Management Based Evolutionary Algorithm for Large-Scale Multi-Modal Multi-Objective Optimization

Large-scale multi-modal multi-objective optimization problems (large-scale MMOPs) refer to scenarios where multiple significantly different Pareto optimal solutions exist in a high-dimensional decision space, exhibiting identical or similar performance in the objective space. Existing multi-modal multi-objective evolutionary algorithms (MMEAs) usually employ multiple subpopulations to approximate different Pareto optimal solutions when solving large-scale MMOPs. However, these MMEAs often fail to consider the optimization performance of each subpopulation when allocating computational resources, instead distributing resources equally among all subpopulations. This approach struggles to simultaneously approximate multiple equivalent Pareto optimal solutions in high-dimensional decision spaces, thereby limiting the efficiency and effectiveness of these algorithms. Building on the general idea of computational resource management, this paper develops a resource allocation approach based MMEA that dynamically allocates computational resources to each subpopulation based on its optimization performance. Additionally, the proposed algorithm can determine whether to merge subpopulations to reduce resource wastage or create new subpopulations to explore potential equivalent Pareto optimal solutions, based on the distribution characteristics of all subpopulations. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art MMEAs in terms of both solutions’ optimality in the objective space and diversity in the decision space.