Dynamic multi-objective evolutionary algorithm based on dual-layer collaborative prediction under multiple perspective
Prediction-based strategies become increasingly prominent in addressing dynamic multi-objective optimization problems (DMOPs). However, challenges remain in selecting predictive models and effectively utilizing historical solutions. In this paper, we propose a multiple perspective dual-layer collabo...
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| Vydáno v: | Swarm and evolutionary computation Ročník 94; s. 101876 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.04.2025
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| ISSN: | 2210-6502 |
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| Abstract | Prediction-based strategies become increasingly prominent in addressing dynamic multi-objective optimization problems (DMOPs). However, challenges remain in selecting predictive models and effectively utilizing historical solutions. In this paper, we propose a multiple perspective dual-layer collaborative prediction strategy to efficiently tackle both challenges. The multi-perspective approach is further divided into a search perspective and a spatial perspective and realized through the collaboration of three sub-strategies. From the search perspective, we employ a dual-layer prediction strategy that focuses on both global and local information. Specifically, the first layer utilizes Gaussian process regression (GPR) to predict centrality, which serves as a measure of the population’s collective intelligence. This layer effectively captures global insights into population dynamics, identifying overarching movement trends over time. Building on these global insights, the second layer employs a knee-point interval partitioning strategy that combines vector partitioning with knee-point-based predictions. This layer provides localized insights that complement the broader movement trends identified by the first layer. From the spatial perspective, we implement dual-layer historical similarity detection across non-dominated solutions in both decision and objective spaces. Specifically, the historical Pareto-similarity selection strategy identifies populations in these spaces that demonstrate the greatest similarity to the current population’s non-dominated solutions. The spatial perspective complements the search perspective, forming a coherent framework that systematically integrates global, local, and historical information. Experimental results indicate that the proposed algorithm performs better than previous state-of-the-art methods.
•In this paper, we propose the multiple perspective dual-layer collaborative prediction strategy to efficiently tackle both challenges. The multi-perspective approach is further divided into a search perspective and a spatial perspective realized through the collaboration of three sub-strategies.•From the search perspective, we employ a dual-layer prediction strategy that focuses on both global and local information. Specifically, the first layer utilizes Gaussian process regression (GPR) to predict centrality, which serves as a measure of the population’s collective intelligence. This layer effectively captures global insights into population dynamics, identifying overarching movement trends over time.•From the spatial perspective, we implement dual-layer historical similarity detection across non-dominated solutions in both decision and objective spaces. Specifically, the historical Pareto-similarity selection strategy identifies populations in these spaces that demonstrate the greatest similarity to the current population’s non-dominated solutions. |
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| AbstractList | Prediction-based strategies become increasingly prominent in addressing dynamic multi-objective optimization problems (DMOPs). However, challenges remain in selecting predictive models and effectively utilizing historical solutions. In this paper, we propose a multiple perspective dual-layer collaborative prediction strategy to efficiently tackle both challenges. The multi-perspective approach is further divided into a search perspective and a spatial perspective and realized through the collaboration of three sub-strategies. From the search perspective, we employ a dual-layer prediction strategy that focuses on both global and local information. Specifically, the first layer utilizes Gaussian process regression (GPR) to predict centrality, which serves as a measure of the population’s collective intelligence. This layer effectively captures global insights into population dynamics, identifying overarching movement trends over time. Building on these global insights, the second layer employs a knee-point interval partitioning strategy that combines vector partitioning with knee-point-based predictions. This layer provides localized insights that complement the broader movement trends identified by the first layer. From the spatial perspective, we implement dual-layer historical similarity detection across non-dominated solutions in both decision and objective spaces. Specifically, the historical Pareto-similarity selection strategy identifies populations in these spaces that demonstrate the greatest similarity to the current population’s non-dominated solutions. The spatial perspective complements the search perspective, forming a coherent framework that systematically integrates global, local, and historical information. Experimental results indicate that the proposed algorithm performs better than previous state-of-the-art methods.
•In this paper, we propose the multiple perspective dual-layer collaborative prediction strategy to efficiently tackle both challenges. The multi-perspective approach is further divided into a search perspective and a spatial perspective realized through the collaboration of three sub-strategies.•From the search perspective, we employ a dual-layer prediction strategy that focuses on both global and local information. Specifically, the first layer utilizes Gaussian process regression (GPR) to predict centrality, which serves as a measure of the population’s collective intelligence. This layer effectively captures global insights into population dynamics, identifying overarching movement trends over time.•From the spatial perspective, we implement dual-layer historical similarity detection across non-dominated solutions in both decision and objective spaces. Specifically, the historical Pareto-similarity selection strategy identifies populations in these spaces that demonstrate the greatest similarity to the current population’s non-dominated solutions. |
| ArticleNumber | 101876 |
| Author | Li, Jun Hu, Yaru Zheng, Jinhua Ou, Junwei Li, Yana Peng, Jiankang |
| Author_xml | – sequence: 1 givenname: Yaru surname: Hu fullname: Hu, Yaru email: huyaru@xtu.edu.cn organization: School of Computer Science, Xiangtan University, Xiangtan 411105, China – sequence: 2 givenname: Yana surname: Li fullname: Li, Yana email: liyana2024@163.com organization: School of Computer Science, Xiangtan University, Xiangtan 411105, China – sequence: 3 givenname: Junwei orcidid: 0000-0001-8769-0953 surname: Ou fullname: Ou, Junwei email: junweiou@xtu.edu.com organization: School of Computer Science, Xiangtan University, Xiangtan 411105, China – sequence: 4 givenname: Jiankang surname: Peng fullname: Peng, Jiankang email: pengjiankang01@gmail.com organization: School of Computer Science, Xiangtan University, Xiangtan 411105, 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, Xiangtan University, Xiangtan 411105, China |
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| Keywords | Gaussian process regression Prediction-based strategies Knee-point interval partitioning Historical similarity detection |
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