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Efficient three-stage surrogate-assisted differential evolution for expensive optimization problems
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AI
DOI：10.1016/j.swevo.2025.102093.png)
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Abstract
En 中文
• Proposed dynamic search strategy for efficient exploration of search space. • Combined RBF and Lipschitz matrix-based surrogate model to guide optimization. • Novel full mutation strategy accelerates convergence and prevents stagnation. • Surrogate-assisted local search ensures fine-grained optimization and speed.
Keywords:
dynamic search strategy
RBF and Lipschitz surrogate model
full mutation strategy
surrogate-assisted local search
efficient exploration
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