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KT-EGO: a knowledge transfer assisted efficient global optimization algorithm for solving high-dimensional expensive black-box problems.
- Source :
- Engineering Optimization; Dec2023, Vol. 55 Issue 12, p2015-2033, 19p
- Publication Year :
- 2023
-
Abstract
- Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, a knowledge transfer (KT) assisted efficient global optimization (EGO) algorithm is proposed, called the 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, a surrogate-based data fusion strategy is proposed in the 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 the proposed algorithm, it is compared against state-of-the-art algorithms over 12 benchmark functions and a 28-dimensional engineering optimization for the design of a compressor blade, which fully validates the effectiveness of the KT-EGO for solving HEB problems. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0305215X
- Volume :
- 55
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Engineering Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 173686974
- Full Text :
- https://doi.org/10.1080/0305215X.2022.2139374