{"title":"基于协同知识转移的多目标多任务粒子群优化","authors":"Yushuang Wang , Zheng Liu , Honggui Han","doi":"10.1016/j.swevo.2025.102115","DOIUrl":null,"url":null,"abstract":"<div><div>Evolutionary multitask optimization (EMTO) has been an emerging optimization paradigm to handle several different optimization problems in parallel by utilizing knowledge transfer. However, most existing EMTO algorithms focus only on facilitating knowledge transfer in the search space to deal with multiple optimization tasks, while ignoring the potential relationship problem in the objective space, which may lead to the degradation of knowledge transfer performance, especially for multiobjective EMTO. To address this problem, a collaborative knowledge transfer-based multiobjective multitask particle swarm optimization (CKT-MMPSO) is designed in this paper. First, a CKT-MMPSO scheme is introduced to comprehensively exploit the knowledge from different spaces to solve multiple optimization problems. Then, the knowledge transfer can be effectively implemented to improve the quality of solutions. Second, a bi-space knowledge reasoning method is developed to make full use of population distribution information in the search space and particle evolutionary information in the objective space. Then, the search space knowledge and the objective space knowledge can be acquired to assist in the knowledge transfer. Third, an information entropy-based collaborative knowledge transfer mechanism is designed to balance convergence and diversity. Then, knowledge transfer patterns can be adaptively performed in different evolutionary stages to generate promising solutions. Finally, CKT-MMPSO is applied to some benchmark problems to verify its effectiveness. Furthermore, compared with other state-of-the-art algorithms, several experiments demonstrate that CKT-MMPSO can achieve the desirable performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102115"},"PeriodicalIF":8.5000,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Collaborative knowledge transfer-based multiobjective multitask particle swarm optimization\",\"authors\":\"Yushuang Wang , Zheng Liu , Honggui Han\",\"doi\":\"10.1016/j.swevo.2025.102115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Evolutionary multitask optimization (EMTO) has been an emerging optimization paradigm to handle several different optimization problems in parallel by utilizing knowledge transfer. However, most existing EMTO algorithms focus only on facilitating knowledge transfer in the search space to deal with multiple optimization tasks, while ignoring the potential relationship problem in the objective space, which may lead to the degradation of knowledge transfer performance, especially for multiobjective EMTO. To address this problem, a collaborative knowledge transfer-based multiobjective multitask particle swarm optimization (CKT-MMPSO) is designed in this paper. First, a CKT-MMPSO scheme is introduced to comprehensively exploit the knowledge from different spaces to solve multiple optimization problems. Then, the knowledge transfer can be effectively implemented to improve the quality of solutions. Second, a bi-space knowledge reasoning method is developed to make full use of population distribution information in the search space and particle evolutionary information in the objective space. Then, the search space knowledge and the objective space knowledge can be acquired to assist in the knowledge transfer. Third, an information entropy-based collaborative knowledge transfer mechanism is designed to balance convergence and diversity. Then, knowledge transfer patterns can be adaptively performed in different evolutionary stages to generate promising solutions. Finally, CKT-MMPSO is applied to some benchmark problems to verify its effectiveness. Furthermore, compared with other state-of-the-art algorithms, several experiments demonstrate that CKT-MMPSO can achieve the desirable performance.</div></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":\"98 \",\"pages\":\"Article 102115\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2025-08-05\",\"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/S2210650225002731\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225002731","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}
Evolutionary multitask optimization (EMTO) has been an emerging optimization paradigm to handle several different optimization problems in parallel by utilizing knowledge transfer. However, most existing EMTO algorithms focus only on facilitating knowledge transfer in the search space to deal with multiple optimization tasks, while ignoring the potential relationship problem in the objective space, which may lead to the degradation of knowledge transfer performance, especially for multiobjective EMTO. To address this problem, a collaborative knowledge transfer-based multiobjective multitask particle swarm optimization (CKT-MMPSO) is designed in this paper. First, a CKT-MMPSO scheme is introduced to comprehensively exploit the knowledge from different spaces to solve multiple optimization problems. Then, the knowledge transfer can be effectively implemented to improve the quality of solutions. Second, a bi-space knowledge reasoning method is developed to make full use of population distribution information in the search space and particle evolutionary information in the objective space. Then, the search space knowledge and the objective space knowledge can be acquired to assist in the knowledge transfer. Third, an information entropy-based collaborative knowledge transfer mechanism is designed to balance convergence and diversity. Then, knowledge transfer patterns can be adaptively performed in different evolutionary stages to generate promising solutions. Finally, CKT-MMPSO is applied to some benchmark problems to verify its effectiveness. Furthermore, compared with other state-of-the-art algorithms, several experiments demonstrate that CKT-MMPSO can achieve the desirable performance.
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.