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Dynamic multi-objective optimization algorithm via historical collaborative strategy and interval prediction strategy
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AI
DOI：10.1016/j.swevo.2026.102281.png)
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Abstract
En 中文
Dynamic multi-objective optimization problems (DMOPs) involve scenarios where objective functions, decision variables, parameters, or other elements vary over time. An effective approach to address DMOPs is to integrate algorithms designed for static multi-objective optimization problems with dynamic response strategies. To improve the performance of these strategies in terms of both population diversity and convergence, this paper proposes a novel dynamic response strategy, the historical collaborative and interval prediction strategy (HCIPS). When confronted with environmental changes, we conduct a three-level population analysis: overall, historical, and individual. Firstly, the interval-based response strategy identifies interval partitioning of a population at time t, enabling global localization of the predicted population and effectively preserving diverse population information. Secondly, the history-based response strategy guides the population movement by selecting optimal solutions from historical populations. Thirdly, the individual-based response strategy predicts individual positions by tracking the movement of key points. This serves as a crucial complement to the history-based response strategy, compensating for its primary drawback: a lack of sufficient historical data in the early stages of evolution. Experimental results indicate that the HCIPS offers advantages in solving DMOPs compared to past state-of-the-art algorithms.
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