A Multipopulation Evolutionary Algorithm for Solving Large-Scale Multimodal Multiobjective Optimization Problems

Multimodal multiobjective optimization problems (MMOPs) widely exist in real-world applications, which have multiple equivalent Pareto-optimal solutions that are similar the objective space but totally different decision space. While some evolutionary algorithms (EAs) been developed to find recent years, they ineffective handle large-scale MMOPs having a large number of variables. This article thus proposes an EA for solving with sparse solutions, i.e., most variables optimal 0. The proposed algorithm explores regions via subpopulations and guides search behavior adaptively updated guiding vectors. vector each subpopulation not only provides efficient convergence huge also differentiates its direction from others multimodality. existing EAs solve 2-7 variables, is shown be effective benchmark up 500 Moreover, produces better result than state-of-the-art methods neural architecture search.