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Order-based error for managing ensembles of surrogates in mesh adaptive direct search

Author

Listed:
  • Charles Audet

    (École Polytechnique de Montréal)

  • Michael Kokkolaras

    (McGill University)

  • Sébastien Le Digabel

    (École Polytechnique de Montréal)

  • Bastien Talgorn

    (McGill University)

Abstract

We investigate surrogate-assisted strategies for global derivative-free optimization using the mesh adaptive direct search (MADS) blackbox optimization algorithm. In particular, we build an ensemble of surrogate models to be used within the search step of MADS to perform global exploration, and examine different methods for selecting the best model for a given problem at hand. To do so, we introduce an order-based error tailored to surrogate-based search. We report computational experiments for ten analytical benchmark problems and three engineering design applications. Results demonstrate that different metrics may result in different model choices and that the use of order-based metrics improves performance.

Suggested Citation

  • Charles Audet & Michael Kokkolaras & Sébastien Le Digabel & Bastien Talgorn, 2018. "Order-based error for managing ensembles of surrogates in mesh adaptive direct search," Journal of Global Optimization, Springer, vol. 70(3), pages 645-675, March.
    • Handle: RePEc:spr:jglopt:v:70:y:2018:i:3:d:10.1007_s10898-017-0574-1
    DOI: 10.1007/s10898-017-0574-1
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    References listed on IDEAS

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    Cited by:

    1. Charles Audet & Sébastien Le Digabel & Renaud Saltet, 2022. "Quantifying uncertainty with ensembles of surrogates for blackbox optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 29-66, September.
    2. Charles Audet & Christophe Tribes, 2018. "Mesh-based Nelder–Mead algorithm for inequality constrained optimization," Computational Optimization and Applications, Springer, vol. 71(2), pages 331-352, November.
    3. Charles Audet & Jean Bigeon & Romain Couderc, 2021. "Combining Cross-Entropy and MADS Methods for Inequality Constrained Global Optimization," SN Operations Research Forum, Springer, vol. 2(3), pages 1-26, September.

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