A historical surrogate model ensemble assisted Bayesian evolutionary optimization algorithm for solving expensive many-objective problems

Hui Liu

doi:10.1016/j.asoc.2025.113367

2025-06-11

PRE

DOI：10.1016/j.asoc.2025.113367

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摘要

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• 利用历史模型提高精度而不增加成本。 • 多模型辅助选择以改善种群搜索。 • 针对高维多目标问题的定制化环境选择策略。 • 双策略确保优化过程的收敛性与分布性。 • 基于欧几里得距离的所提填充采样策略可增强优化效果及全局精度。

期刊

Applied Soft Computing

IF:

6.6

论文数:

1.4W

被引数:

4.8W

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