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A historical surrogate model ensemble assisted Bayesian evolutionary optimization algorithm for solving expensive many-objective problems
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DOI：10.1016/j.asoc.2025.113367.png)
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Abstract
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• Leveraging historical models to boost accuracy without added costs. • Multiple models aid selection for better population search. • Tailored Environment Selection strategy for high-dimensional many-objective problems. • Dual strategy ensures convergence and distribution for optimization. • The proposed infill sampling strategy based on Euclidean Distance enhances optimization, global accuracy.
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6.6
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1.4W
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4.8W
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