{"title":"A Dynamic Knowledge-Guided Coevolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems","authors":"Yingwei Li;Xiang Feng;Huiqun Yu","doi":"10.1109/TSMC.2024.3446624","DOIUrl":null,"url":null,"abstract":"Large-scale sparse multiobjective optimization problems (SMOPs) exist widely in real-world applications, and solving them requires algorithms that can handle high-dimensional decision space while simultaneously discovering the sparse distribution of Pareto optimal solutions. However, it is difficult for most existing multiobjective evolutionary algorithms (MOEAs) to get satisfactory results. To address this problem, this article proposes a dynamic knowledge-guided coevolutionary algorithm, which employs a cooperative coevolutionary framework tailored for large-scale SMOPs. Specifically, variable selection is performed initially for the dimension reduction, and two populations are evolved in the original and reduced decision spaces, respectively. After offspring generation, variable replacement is performed to precisely identify the sparse distribution of Pareto optimal solutions. Furthermore, a dynamic score update mechanism is designed based on the discovered sparsity knowledge, which aims to adjust the direction of evolution dynamically. The superiority of the proposed algorithm is demonstrated by applying it to a variety of benchmark test instances and real-world test instances with the comparison of five other state-of-the-art MOEAs.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Systems Man Cybernetics-Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10664607/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Large-scale sparse multiobjective optimization problems (SMOPs) exist widely in real-world applications, and solving them requires algorithms that can handle high-dimensional decision space while simultaneously discovering the sparse distribution of Pareto optimal solutions. However, it is difficult for most existing multiobjective evolutionary algorithms (MOEAs) to get satisfactory results. To address this problem, this article proposes a dynamic knowledge-guided coevolutionary algorithm, which employs a cooperative coevolutionary framework tailored for large-scale SMOPs. Specifically, variable selection is performed initially for the dimension reduction, and two populations are evolved in the original and reduced decision spaces, respectively. After offspring generation, variable replacement is performed to precisely identify the sparse distribution of Pareto optimal solutions. Furthermore, a dynamic score update mechanism is designed based on the discovered sparsity knowledge, which aims to adjust the direction of evolution dynamically. The superiority of the proposed algorithm is demonstrated by applying it to a variety of benchmark test instances and real-world test instances with the comparison of five other state-of-the-art MOEAs.
期刊介绍:
The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.