A Bayesian approach to constrained single- and multi-objective optimization

This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can...

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Bibliographic Details
Published in:	Journal of global optimization Vol. 67; no. 1-2; pp. 97 - 133
Main Authors:	Feliot, Paul, Bect, Julien, Vazquez, Emmanuel
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2017 Springer Springer Nature B.V Springer Verlag
Subjects:	Algorithms Analysis Approximation Bayesian analysis Computation Computer Science Computer simulation Constraints Criteria Design optimization Machine Learning Mathematical functions Mathematics Mathematics and Statistics Monte Carlo simulation Multiple objective analysis Operations Research Operations Research/Decision Theory Optimization Performance evaluation Real Functions Sampling Simulation Simulation methods Statistics Studies Subset simulation Gaussian process Expected improvement Sequential Monte Carlo Multi-objective Bayesian optimization Kriging sequential Monte Carlo subset simulation kriging Gaussian processes multi-objective optimization expected improvement
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
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