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Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization

Author

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  • Jianyuan Zhai

    (Georgia Institute of Technology)

  • Fani Boukouvala

    (Georgia Institute of Technology)

Abstract

The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-based optimization solvers may be impractical and data-driven optimization techniques are often needed. In this work, we present a novel data-driven spatial branch-and-bound algorithm for simulation-based optimization problems with box constraints, aiming for consistent globally convergent solutions. The main contribution of this paper is the introduction of the concept data-driven convex underestimators of data and surrogate functions, which are employed within a spatial branch-and-bound algorithm. The algorithm is showcased by an illustrative example and is then extensively studied via computational experiments on a large set of benchmark problems.

Suggested Citation

  • Jianyuan Zhai & Fani Boukouvala, 2022. "Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization," Journal of Global Optimization, Springer, vol. 82(1), pages 21-50, January.
    • Handle: RePEc:spr:jglopt:v:82:y:2022:i:1:d:10.1007_s10898-021-01045-8
    DOI: 10.1007/s10898-021-01045-8
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    References listed on IDEAS

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    1. Colin R. Reeves, 1997. "Feature Article---Genetic Algorithms for the Operations Researcher," INFORMS Journal on Computing, INFORMS, vol. 9(3), pages 231-250, August.
    2. Wendy Xu & Barry Nelson, 2013. "Empirical stochastic branch-and-bound for optimization via simulation," IISE Transactions, Taylor & Francis Journals, vol. 45(7), pages 685-698.
    3. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    4. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
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    Cited by:

    1. Kaiwen Ma & Luis Miguel Rios & Atharv Bhosekar & Nikolaos V. Sahinidis & Sreekanth Rajagopalan, 2023. "Branch-and-Model: a derivative-free global optimization algorithm," Computational Optimization and Applications, Springer, vol. 85(2), pages 337-367, June.

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