KT-EGO: A Knowledge Transfer Assisted Efficient Global Optimization Algorithm for Solving High-Dimensional Expensive Black-Box Problems

Abstract:Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, we propose a knowledge transfer assisted efficient global optimization (EGO) algorithm, labeled as KT-EGO, which extends the EGO algorithm for solving problems over higher dimensions (i.e., $d>20$). Specifically, the original design space is divided into several low-dimensional subset design spaces. More importantly, in order to extract information from the subset design spaces to accelerate the progress of full optimization, we propose a surrogate-based data fusion strategy in KT-EGO. And further, a searching strategy with an adaptive variable range is devised to enhance the exploitation of promising areas. To show the effectiveness of our proposed algorithm, it is compared against the state-of-the-art algorithms over 12 benchmark functions and a 28-dimensional engineering optimization for the design of compressor blade, which fully validates the effectiveness of the KT-EGO for solving HEB problems.

Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

arXiv:2609.37473 [cs.CE]

(or arXiv:2609.37473v1 [cs.CE] for this version)

https://doi.org/10.48550/arXiv.2609.37473 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1080/0305215X.2022.2139374 Focus to learn more DOI(s) linking to related resources

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