Large-Scale Multimodal Multiobjective Optimization Based on Multiview Diversity Enhancement Mechanism

Large-scale multimodal multiobjective optimization problems with sparse Pareto optimal solutions pose a significant challenge in the field of optimization, primarily stemming from the multimodality characteristic, the curse of dimensionality, and the uncertainty of sparse solutions. In this work, we propose a sparse large-scale multimodal multiobjective evolutionary algorithm (MVDE-MMEA) to solve such complex tasks. In MVDE-MMEA, a multiview diversity enhancement mechanism is designed to improve the exploration ability of the algorithm across the entire decision space. The diversity mechanism seamlessly transits from a global perspective to a local one as the population evolves, which contributes to an effective convergence towards multiple Pareto optimal sets in large-scale decision space. Furthermore, a clustering method is proposed to divide the population into several niches, guided by the shared characteristics among candidates. In this way, the algorithm shifts its focus from exploration in the whole search space to exploitation in local regions. Experimental studies are conducted on eight benchmark test problems and 12 feature selection problems. The comparative results with the state-of-the-art algorithms demonstrate the superiority of MVDE-MMEA.