Clustering-aided Grid-based One-to-One Selection-driven Evolutionary Algorithm for Multi/Many-objective Optimization

Abstract

Multiobjective evolutionary algorithms are highly effective in solving multiobjective optimization problems (MOPs). The selection strategy, involving mating and environmental selection, is crucial in shaping these algorithms. However, when applied to many-objective optimization (MaOPs) with more than three objectives, existing methods face challenges due to reduced selection pressure and issues in maintaining diversity, making them less efficient. To address these challenges, we present a novel approach in this paper: the Clustering-aided Grid-Based One-to-One Selection-driven Evolutionary Algorithm (ClGrMOEA), designed to handle both MOPs and MaOPs effectively. In ClGrMOEA, we introduce a hybrid approach that combines a novel clustering-based mating selection, utilizing K-means clustering and Euclidean distance-based convergence indicators, with grid-based one-to-one environmental selection, merging Pareto dominance with grid-based selection. Extensive experiments are conducted on 19 benchmark problems and 16 real-world problems to validate the superior performance of ClGrMOEA compared to seven state-ofthe-art algorithms. The experimental results demonstrate that ClGrMOEA significantly outperforms these benchmark algorithms.