Palakonda Vikas, Ghorbanpour Samira, Kang Jae-Mo, Jung Heechul
Department of Artificial Intelligence, Kyungpook National University, Daegu, South Korea.
Bharti School of Engineering and Computer Science, Laurentian University, Sudbury, ON, Canada.
Sci Rep. 2024 Nov 22;14(1):29006. doi: 10.1038/s41598-024-76877-x.
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.
差分进化（DE）是一种用于解决单目标和多目标优化问题（MOP）的强大进化算法。虽然存在众多多目标差分进化（MODE）变体，但先前的研究主要集中在参数控制和变异算子上，常常忽略了目标空间中种群分布不足的问题。本文提出了一种用于多目标优化的外部存档引导径向网格驱动差分进化算法（Ar-RGDEMO）来应对这些挑战。所提出的Ar-RGDEMO包含三个关键组件：一种新颖的变异算子，它将径向网格驱动策略与从帕累托前沿估计得出的性能指标相结合；一种截断过程，该过程采用帕累托支配并结合基于候选解之间的移位相似距离的排序策略；以及一个外部存档，该存档使用聚类方法保存精英个体。在四组基准问题上的实验结果表明，与文献中的七种最先进算法相比，所提出的Ar-RGDEMO表现出具有竞争力或更优的性能。
