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

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2024-09-21 DOI:10.1016/j.asoc.2024.112272

Jing Wang , Shunce Mei , Changxin Liu , Hu Peng , Zhijian Wu

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引用次数: 0

Abstract

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.

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使用无穷小方法的基于分解的多目标进化算法

基于分解的多目标进化算法（MOEA/D）已被广泛用于解决现实世界中的各种难题，并表现出卓越的性能。然而，事实证明，初始集合权向量并不适合具有复杂帕累托前沿（PF）结构的多目标优化问题（MOP），而且 MOEA/D 解决这些不规则 MOP 时的求解性能会受到很大影响。有鉴于此，本文提出了一种利用无穷小方法的改进型 MOEA/D 算法。该算法结合了无穷小法中的全局分解概念，精简了 PF 的特征信息，从而便于调整权重向量，使其达到最佳分布。因此，提高了资源分配效率和算法性能。在实证研究中，对 ZDT、DTLZ 和 WFG 测试服中的 28 个基准进行了性能测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.