A Stochastic Adaptive Radial Basis Function Algorithm for Costly Black-Box Optimization

In this paper, we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions. The exploration radii in local searches are generated adaptively. Each iteration point is selected from some randomly generated trial points according t...

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Bibliographic Details
Published inJournal of the Operations Research Society of China (Internet) Vol. 6; no. 4; pp. 587 - 609
Main Authors Zhou, Zhe, Bai, Fu-Sheng
Format Journal Article
LanguageEnglish
Published Beijing Operations Research Society of China 01.12.2018
Springer Nature B.V
Subjects
Adaptive algorithms
Algorithms
Black boxes
Global optimization
Iterative methods
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Optimization
Radial basis function
Restarting
Radial basis function
Global optimization
90C56
Stochastic algorithm
90C26
90C59
Costly black-box optimization
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ISSN2194-668X
2194-6698
DOI10.1007/s40305-018-0204-8

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Summary:In this paper, we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions. The exploration radii in local searches are generated adaptively. Each iteration point is selected from some randomly generated trial points according to certain criteria. A restarting strategy is adopted to build the restarting version of the algorithm. The performance of the presented algorithm and its restarting version are tested on 13 standard numerical examples. The numerical results suggest that the algorithm and its restarting version are very effective.
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ISSN:2194-668X
2194-6698
DOI:10.1007/s40305-018-0204-8