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. The proposed ClGrMOEA introduces a hybrid approach that combines clustering-based mating selection with grid-based one-to-one environmental selection to balance convergence and diversity in MOPs and MaOPs. The mating selection employs K-means clustering to partition the objective space and utilizes a convergence indicator based on Euclidean distance to select promising solutions for offspring generation. The environmental selection combines Pareto dominance with grid-based one-to-one selection, using grid coordinate point distance to select promising solutions. An external archive based on Pareto dominance and crowding distance preserves elite individuals. Extensive experiments are conducted on 19 benchmark problems and 16 real-world problems to validate the superior performance of ClGrMOEA compared to seven state-of-the-art algorithms. The experimental results demonstrate that ClGrMOEA significantly outperforms these benchmark algorithms.

Keywords

• Convergence
• diversity
• multi-objective optimization
• many-objective optimization
• K-means clustering
• grid settings