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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| Veröffentlicht in: | Swarm and evolutionary computation Jg. 94; S. 101876 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.04.2025
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| Schlagworte: | |
| ISSN: | 2210-6502 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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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| ISSN: | 2210-6502 |
| DOI: | 10.1016/j.swevo.2025.101876 |