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Sequential model based optimization of partially defined functions under unknown constraints

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  • Candelieri Antonio

    (University of Milano-Bicocca)

Abstract

This paper presents a sequential model based optimization framework for optimizing a black-box, multi-extremal and expensive objective function, which is also partially defined, that is it is undefined outside the feasible region. Furthermore, the constraints defining the feasible region within the search space are unknown. The approach proposed in this paper, namely SVM-CBO, is organized in two consecutive phases, the first uses a Support Vector Machine classifier to approximate the boundary of the unknown feasible region, the second uses Bayesian Optimization to find a globally optimal solution within the feasible region. In the first phase the next point to evaluate is chosen by dealing with the trade-off between improving the current estimate of the feasible region and discovering possible disconnected feasible sub-regions. In the second phase, the next point to evaluate is selected as the minimizer of the Lower Confidence Bound acquisition function but constrained to the current estimate of the feasible region. The main of the paper is a comparison with a Bayesian Optimization process which uses a fixed penalty value for infeasible function evaluations, under a limited budget (i.e., maximum number of function evaluations). Results are related to five 2D test functions from literature and 80 test functions, with increasing dimensionality and complexity, generated through the Emmental-type GKLS software. SVM-CBO proved to be significantly more effective as well as computationally efficient.

Suggested Citation

  • Candelieri Antonio, 2021. "Sequential model based optimization of partially defined functions under unknown constraints," Journal of Global Optimization, Springer, vol. 79(2), pages 281-303, February.
    • Handle: RePEc:spr:jglopt:v:79:y:2021:i:2:d:10.1007_s10898-019-00860-4
    DOI: 10.1007/s10898-019-00860-4
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    References listed on IDEAS

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    1. Paul Feliot & Julien Bect & Emmanuel Vazquez, 2017. "A Bayesian approach to constrained single- and multi-objective optimization," Journal of Global Optimization, Springer, vol. 67(1), pages 97-133, January.
    2. G. Di Pillo & G. Liuzzi & S. Lucidi & V. Piccialli & F. Rinaldi, 2016. "A DIRECT-type approach for derivative-free constrained global optimization," Computational Optimization and Applications, Springer, vol. 65(2), pages 361-397, November.
    3. A. Candelieri & R. Perego & F. Archetti, 2018. "Bayesian optimization of pump operations in water distribution systems," Journal of Global Optimization, Springer, vol. 71(1), pages 213-235, May.
    4. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    5. Gianni Pillo & Stefano Lucidi & Francesco Rinaldi, 2015. "A Derivative-Free Algorithm for Constrained Global Optimization Based on Exact Penalty Functions," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 862-882, March.
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