A decomposition-based multi-objective evolutionary algorithm using infinitesimal method

Multi-Objective Evolutionary Algorithm based on decomposition (MOEA/D) has been extensively employed to address a diverse array of real-world challenges and has shown excellent performance. However, the initial collection of aggregate weight vectors proves unsuitable for multi-objective optimization problems (MOPs) featuring intricate Pareto front (PF) structures, and the solving performance will be greatly affected when MOEA/D solves these irregular MOPs. In light of these challenges, a refined MOEA/D algorithm utilizing infinitesimal method is proposed. This algorithm incorporates the notion of global decomposition stemming from infinitesimal method to streamline the feature information of PF, thereby facilitating the adjustment of the weight vector towards optimal distribution. Consequently, enhancements in resource allocation efficiency and algorithmic performance are achieved. In the empirical investigation, the algorithm’s performance is tested on 28 benchmarks from ZDT,DTLZ and WFG test suits.Wilcoxon’s rank-sum test and Fredman’s test were carried out on performance metrics, which proved that the proposed MOEA/D-DKS was superior to other comparison algorithms.