Global optimization of expensive black box problems with a known lower bound

In this paper we propose an algorithm for the global optimization of computationally expensive black-box functions. For this class of problems, no information, like e.g. the gradient, can be obtained and function evaluation is highly expensive. In many applications, however, a lower bound on the obj...

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Bibliographic Details
Published in:Journal of global optimization 2022-05, Vol.57 (1), p.177
Main Authors: Cassioli, Andrea, Schoen, Fabio
Format: Article
Language:English
Subjects:
Algorithms
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Summary:In this paper we propose an algorithm for the global optimization of computationally expensive black-box functions. For this class of problems, no information, like e.g. the gradient, can be obtained and function evaluation is highly expensive. In many applications, however, a lower bound on the objective function is known; in this situation we derive a modified version of the algorithm introduced in Gutmann (J Glob Optim 19:201227, 2001). Using this information produces a significant improvement in the quality of the resulting method, with only a small increase in the computational cost. Extensive computational results are provided which support this statement. Keywords Global optimization * Black-box function * Expensive objective functions * Radial basis method * Bumpiness
ISSN:0925-5001