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Addressing mixed constraints: an improved framework for black-box optimization

Author

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  • Raju Chowdhury

    (Indian Institute of Technology Madras)

  • Neelesh S. Upadhye

    (Indian Institute of Technology Madras)

Abstract

This article proposes a novel hybrid Bayesian optimization framework designed to solve problems with equality, inequality constraints, and their combinations. We develop a new variant of the expected improvement acquisition function that effectively addresses constraints during iteration. This function balances exploration and exploitation within the constrained search space. Our framework adeptly manages inequality and equality constraints and can initiate the iterative process even from infeasible starting points. Finally, we evaluate our proposed framework against existing approaches using synthetic test problems and a real-world engineering design and hydrology problem. By integrating Bayesian optimization techniques with advanced constraint handling, our framework provides a promising avenue for addressing mixed constraints in black-box optimization scenarios.

Suggested Citation

  • Raju Chowdhury & Neelesh S. Upadhye, 2025. "Addressing mixed constraints: an improved framework for black-box optimization," Journal of Global Optimization, Springer, vol. 92(4), pages 889-908, August.
    • Handle: RePEc:spr:jglopt:v:92:y:2025:i:4:d:10.1007_s10898-025-01486-5
    DOI: 10.1007/s10898-025-01486-5
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    References listed on IDEAS

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    1. Gramacy, Robert B., 2016. "laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i01).
    2. Gramacy, Robert B., 2007. "tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i09).
    3. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
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