An improved high-dimensional Bayesian optimization algorithm

The Bayesian Optimization Algorithm, as an effective approach to addressing non-linear global optimization problems, is widely embraced in a myriad of machine learning application domains. With the development of big data, the presence of computational and statistical challenges in high-dimensional...

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Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 55; číslo 13; s. 915
Hlavní autoři: Guan, Juan, Wang, Yanhua
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2025
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Bayesian analysis
Clustering
Computer Science
Datasets
Decomposition
Design optimization
Global optimization
Machine learning
Machines
Manufacturing
Mechanical Engineering
Methods
Optimization algorithms
Principal components analysis
Processes
KPCA
High-dimensional Bayesian optimization
MIMIC algorithm
Variable interaction
ISSN:0924-669X, 1573-7497
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Shrnutí:The Bayesian Optimization Algorithm, as an effective approach to addressing non-linear global optimization problems, is widely embraced in a myriad of machine learning application domains. With the development of big data, the presence of computational and statistical challenges in high-dimensional settings means that, despite the proposed improvements and enhancements, the applicability of the Bayesian Optimization Algorithm is still restricted to low-dimensional problems. Our algorithm (1) extracts an interesting nonlinear latent structure in the function by Kernal Principal Component Analysis(KPCA) to reduce the computational complexity, and (2) uses an improved Mutual-Information-Maximizing Input Clustering (MIMIC) algorithm to optimize only a low-dimensional subspace each iteration for more efficient and effective BO. The experiments demonstrate that the proposed algorithm can achieve a clear improvement in optimization accuracy and speed in high-dimensional space and can efficiently solve high-dimensional problems for Bayesian optimization algorithm.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-025-06750-5