A multi-population co-evolutionary algorithm based on dual-space division for dynamic multi-objective optimization problems
Effective optimization algorithms are urgently needed for dynamic multi-objective optimization problems (DMOPs) to efficiently track the Pareto optimal front (POF) or Pareto optimal set (POS) in response to environmental changes. This paper proposes a multi-population co-evolutionary algorithm based...
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| Published in: | Information sciences Vol. 728; p. 122712 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.02.2026
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| ISSN: | 0020-0255 |
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| Abstract | Effective optimization algorithms are urgently needed for dynamic multi-objective optimization problems (DMOPs) to efficiently track the Pareto optimal front (POF) or Pareto optimal set (POS) in response to environmental changes. This paper proposes a multi-population co-evolutionary algorithm based on dual-space division (MPDS) for solving DMOPs, which aims to achieve rapid convergence while maintaining population diversity. The algorithm divides the subpopulations within both the objective space and the decision space, integrating information from both spaces to more accurately predict the updated positions of the new population after environmental changes. The algorithm consists of three essential steps: first, it divides the subpopulations within both the objective space and the decision space after detecting an environmental change. Second, new individuals adapted to the updated POF are generated based on the evolutionary directions of the center points in each subpopulation, promoting rapid population convergence. Meanwhile, to prevent the population from getting trapped in local optima, new individuals are randomly generated within a certain region of the subpopulation to enhance diversity. Third, the original population is combined with the two populations generated in the previous steps. Finally, the solutions with good convergence and diversity are selected after performing a non-dominated sort on the combined solutions. Experiments demonstrate that our algorithm outperforms five state-of-the-art algorithms in terms of balancing convergence and diversity.
•The paper proposes a dynamic multi-objective optimization algorithm based on historical collaborative strategy and interval prediction strategy.•The response strategy determines the direction of population movement by selecting optimal solutions from the historical population.•The individual-based response strategy predicts the positions of individuals in a population by considering the movement direction of special point. |
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| AbstractList | Effective optimization algorithms are urgently needed for dynamic multi-objective optimization problems (DMOPs) to efficiently track the Pareto optimal front (POF) or Pareto optimal set (POS) in response to environmental changes. This paper proposes a multi-population co-evolutionary algorithm based on dual-space division (MPDS) for solving DMOPs, which aims to achieve rapid convergence while maintaining population diversity. The algorithm divides the subpopulations within both the objective space and the decision space, integrating information from both spaces to more accurately predict the updated positions of the new population after environmental changes. The algorithm consists of three essential steps: first, it divides the subpopulations within both the objective space and the decision space after detecting an environmental change. Second, new individuals adapted to the updated POF are generated based on the evolutionary directions of the center points in each subpopulation, promoting rapid population convergence. Meanwhile, to prevent the population from getting trapped in local optima, new individuals are randomly generated within a certain region of the subpopulation to enhance diversity. Third, the original population is combined with the two populations generated in the previous steps. Finally, the solutions with good convergence and diversity are selected after performing a non-dominated sort on the combined solutions. Experiments demonstrate that our algorithm outperforms five state-of-the-art algorithms in terms of balancing convergence and diversity.
•The paper proposes a dynamic multi-objective optimization algorithm based on historical collaborative strategy and interval prediction strategy.•The response strategy determines the direction of population movement by selecting optimal solutions from the historical population.•The individual-based response strategy predicts the positions of individuals in a population by considering the movement direction of special point. |
| ArticleNumber | 122712 |
| Author | Li, Jun Xia, Jiaru Hu, Yaru Zheng, Jinhua Ou, Junwei Zhang, Yue Wang, Gaige Song, Yanjie |
| Author_xml | – sequence: 1 givenname: Yaru surname: Hu fullname: Hu, Yaru email: yaruhu@xtu.edu.cn organization: School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China – sequence: 2 givenname: Jiaru surname: Xia fullname: Xia, Jiaru email: xiajiaru25@gmail.com organization: School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China – sequence: 3 givenname: Junwei orcidid: 0009-0006-5492-2029 surname: Ou fullname: Ou, Junwei email: junweiou@xtu.edu.cn organization: School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China – sequence: 4 givenname: Yanjie orcidid: 0000-0002-4313-8312 surname: Song fullname: Song, Yanjie email: songyj_2017@163.com organization: School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China – sequence: 5 givenname: Jun surname: Li fullname: Li, Jun email: jli@hnie.edu.cn organization: Hunan Institute of Engineering, Xiangtan 411105, China – sequence: 6 givenname: Jinhua surname: Zheng fullname: Zheng, Jinhua email: jhzheng@xtu.edu.cn organization: School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China – sequence: 7 givenname: Gaige surname: Wang fullname: Wang, Gaige email: wgg@ouc.edu.cn organization: School of Computer Science and Technology, Ocean University of China, Qingdao 266101, China – sequence: 8 givenname: Yue orcidid: 0000-0002-5656-7642 surname: Zhang fullname: Zhang, Yue email: zhangyue1127@buaa.edu.cn organization: School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China |
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| Keywords | Subpopulation division Multi-population co-evolutionary Dynamic multi-objective optimization Multi-population diversity maintenance Prediction |
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