Jing Jiang , Huoyuan Wang , Pingping Tong , Juanjuan Hong , Zhe Liu , Xing Zhuang , Benyue Su , Fei Han
{"title":"A heterogeneous sparsity knowledge guided evolutionary algorithm for sparse large-scale multiobjective optimization","authors":"Jing Jiang , Huoyuan Wang , Pingping Tong , Juanjuan Hong , Zhe Liu , Xing Zhuang , Benyue Su , Fei Han","doi":"10.1016/j.swevo.2025.102000","DOIUrl":null,"url":null,"abstract":"<div><div>Research on sparse large-scale multiobjective optimization problems (LSMOPs) is rapidly growing due to their diverse applications in science and engineering. Existing studies typically employ static or dynamic knowledge to guide the search in evolutionary algorithms for solving sparse LSMOPs. However, relying solely on a single type of sparsity knowledge may result in ambiguous guidance and suboptimal optimization. To address this, we propose an evolutionary algorithm based on heterogeneous sparsity knowledge (HSKEA). In this approach, static knowledge is represented by a scoring vector to assess the importance of each decision variable, while dynamic knowledge is captured by indicator vectors that identify whether a decision variable is zero or non-zero and iteratively updates based on the population distribution. Two types of populations, including main and auxiliary populations, are initialized using both dynamic and static knowledge and evolved through a new genetic operator guided by heterogeneous sparsity knowledge and information sharing. Experimental results comparing HSKEA to four state-of-the-art algorithms across eight benchmark test problems and three real-world scenarios demonstrate its effectiveness.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102000"},"PeriodicalIF":8.2000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225001580","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Research on sparse large-scale multiobjective optimization problems (LSMOPs) is rapidly growing due to their diverse applications in science and engineering. Existing studies typically employ static or dynamic knowledge to guide the search in evolutionary algorithms for solving sparse LSMOPs. However, relying solely on a single type of sparsity knowledge may result in ambiguous guidance and suboptimal optimization. To address this, we propose an evolutionary algorithm based on heterogeneous sparsity knowledge (HSKEA). In this approach, static knowledge is represented by a scoring vector to assess the importance of each decision variable, while dynamic knowledge is captured by indicator vectors that identify whether a decision variable is zero or non-zero and iteratively updates based on the population distribution. Two types of populations, including main and auxiliary populations, are initialized using both dynamic and static knowledge and evolved through a new genetic operator guided by heterogeneous sparsity knowledge and information sharing. Experimental results comparing HSKEA to four state-of-the-art algorithms across eight benchmark test problems and three real-world scenarios demonstrate its effectiveness.
期刊介绍:
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.