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FIAMS-DMOEA: A Dynamic Multiobjective Evolutionary Algorithm for Highly Uncertain Environmental Changes
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DOI：10.1109/TCSS.2026.3654796.png)
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Abstract
En 中文
Dynamic multiobjective optimization problems (DMOPs) frequently arise in real-world applications, where environments evolve over time. A central challenge in solving DMOPs is the ability to accurately and efficiently track optimal solutions as the environment changes. Existing dynamic multiobjective evolutionary algorithms (DMOEAs) based on single-strategy mechanisms often perform well only under specific environmental conditions, while multistrategy cooperative frameworks tend to suffer from delayed decision-making in rapidly changing, highly uncertain scenarios To address these shortcomings, this article introduces a fuzzy inference-based adaptive multistrategy-DMOEAs (FIAMS-DMOEA). This approach is designed to enhance real-time responsiveness to sudden environmental changes and mitigate decision-making latency. In the proposed method, a randomly dynamic fuzzy system serves as the foundation for population-level adaptability, enabling the algorithm to cope with highly uncertain environmental dynamics. This is further augmented by a multistrategy adaptive response mechanism (MSAR) that improves overall adaptability. By leveraging fuzzy rules, the algorithm can perceive environmental uncertainty in real time and design differentiated migration strategies, thereby establishing a robust foundation for subsequent optimization. The adaptive response mechanism centers on a multistrategy weight update method based on individual dimensional effects, which evaluates individual contributions from a dimension-specific perspective. Extensive experimental results on a suite of 32 benchmark DMOPs demonstrate that FIAMS-DMOEA consistently outperforms five state-of-the-art DMOEAs in overall performance.
Keywords:
Dynamic multiobjective evolutionary algorithm (DMOEAs)
fuzzy inference
multistrategy adaptive response mechanism (MSAR)
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