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A historical search-guided evolutionary framework for dynamic multiobjective optimization
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DOI：10.1016/j.eswa.2025.130878.png)
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Abstract
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• A neural network-based pattern learning strategy is proposed to effectively extract evolutionary patterns from historical environments. • A historical direction-guided evolutionary strategy is presented to guide the static optimization in new environment. • By combining the above two strategies, the HSGE framework is proposed and easily integrated with various dynamic response mechanisms for solving DMOPs.
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