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Abstract
Differential evolution (DE) is a robust evolutionary algorithm for solving single-objective and multi-objective optimization problems (MOPs). While numerous multi-objective DE (MODE) variants exist, prior research has primarily focused on parameter control and mutation operators, often neglecting the issue of inadequate population distribution across the objective space. This paper proposes an external archive-guided radial-grid-driven differential evolution for multi-objective optimization (Ar-RGDEMO) to address these challenges. The proposed Ar-RGDEMO incorporates three key components: a novel mutation operator that integrates a radial-grid-driven strategy with a performance metric derived from Pareto front estimation, a truncation procedure that employs Pareto dominance in conjunction with a ranking strategy based on shifted similarity distances between candidate solutions, and an external archive that preserves elite individuals using a clustering approach. Experimental results on four sets of benchmark problems demonstrate that the proposed Ar-RGDEMO exhibits competitive or superior performance compared to seven state-of-the-art algorithms in the literature.
Published Version
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