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Tuning BARON using derivative-free optimization algorithms

Author

Listed:
  • Jianfeng Liu

    (Carnegie Mellon University)

  • Nikolaos Ploskas

    (Carnegie Mellon University)

  • Nikolaos V. Sahinidis

    (Carnegie Mellon University)

Abstract

Optimization solvers include many options that allow users to control algorithmic aspects that may have a considerable impact on solver performance. Tuning solver options is often necessary to reduce execution time and improve solution quality. Previous studies of solver tuning techniques have focused on mixed-integer linear programming and local nonlinear programming solvers. In this paper, we investigate the potential of tuning a global optimization solver for nonlinear and mixed-integer nonlinear programming problems. In particular, derivative-free optimization (DFO) algorithms are used to find optimal values for options of the global optimization solver BARON. A set of 126 problems from the GLOBALLib and MINLPLib collections are utilized in a computational study from which we conclude that tuning options can improve the default performance of BARON for individual problems and an entire library. Additionally, we present a systematic comparison of 27 DFO solvers in terms of their ability to improve the performance of the global solver. We find that several DFO implementations are much better than others in terms of finding optimal tuning parameters.

Suggested Citation

  • Jianfeng Liu & Nikolaos Ploskas & Nikolaos V. Sahinidis, 2019. "Tuning BARON using derivative-free optimization algorithms," Journal of Global Optimization, Springer, vol. 74(4), pages 611-637, August.
    • Handle: RePEc:spr:jglopt:v:74:y:2019:i:4:d:10.1007_s10898-018-0640-3
    DOI: 10.1007/s10898-018-0640-3
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    References listed on IDEAS

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    1. Fan, Shu-Kai S. & Zahara, Erwie, 2007. "A hybrid simplex search and particle swarm optimization for unconstrained optimization," European Journal of Operational Research, Elsevier, vol. 181(2), pages 527-548, September.
    2. Hvattum, Lars Magnus & Glover, Fred, 2009. "Finding local optima of high-dimensional functions using direct search methods," European Journal of Operational Research, Elsevier, vol. 195(1), pages 31-45, May.
    3. Mustafa Baz & Brady Hunsaker & Oleg Prokopyev, 2011. "How much do we “pay” for using default parameters?," Computational Optimization and Applications, Springer, vol. 48(1), pages 91-108, January.
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    Cited by:

    1. Sophia Voulgaropoulou & Nikolaos Samaras & Nikolaos Ploskas, 2022. "Predicting the Execution Time of the Primal and Dual Simplex Algorithms Using Artificial Neural Networks," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
    2. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.

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