IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v90y2025i3d10.1007_s10589-024-00645-w.html
   My bibliography  Save this article

Inter-DS: a cost saving algorithm for expensive constrained multi-fidelity blackbox optimization

Author

Listed:
  • Stéphane Alarie

    (GERAD
    Hydro-Québec)

  • Charles Audet

    (Polytechnique Montréal
    GERAD)

  • Miguel Diago

    (Hydro-Québec)

  • Sébastien Le Digabel

    (Polytechnique Montréal
    GERAD)

  • Xavier Lebeuf

    (Polytechnique Montréal
    GERAD)

Abstract

This work introduces Inter-DS, a blackbox optimization algorithmic framework for computationally expensive constrained multi-fidelity problems. When applying a direct search method to such problems, the scarcity of feasible points may lead to numerous costly evaluations spent on infeasible points. Our proposed algorithm addresses this issue by leveraging multi-fidelity information, allowing for premature interruption of an evaluation when a point is estimated to be infeasible. These estimations are controlled by a biadjacency matrix, for which we propose a construction. The proposed method acts as an intermediary component bridging any non multi-fidelity direct search solver and a multi-fidelity blackbox problem, giving the user freedom of choice for the solver. A series of computational tests are conducted to validate the approach. The results show a significant improvement in solution quality when an initial feasible starting point is provided. When this condition is not met, the outcomes are contingent upon specific properties of the blackbox.

Suggested Citation

  • Stéphane Alarie & Charles Audet & Miguel Diago & Sébastien Le Digabel & Xavier Lebeuf, 2025. "Inter-DS: a cost saving algorithm for expensive constrained multi-fidelity blackbox optimization," Computational Optimization and Applications, Springer, vol. 90(3), pages 607-629, April.
    • Handle: RePEc:spr:coopap:v:90:y:2025:i:3:d:10.1007_s10589-024-00645-w
    DOI: 10.1007/s10589-024-00645-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-024-00645-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10589-024-00645-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Juliane Müller & Joshua D. Woodbury, 2017. "GOSAC: global optimization with surrogate approximation of constraints," Journal of Global Optimization, Springer, vol. 69(1), pages 117-136, September.
    2. Charles Audet & J. Dennis & Sébastien Digabel, 2010. "Globalization strategies for Mesh Adaptive Direct Search," Computational Optimization and Applications, Springer, vol. 46(2), pages 193-215, June.
    3. Charles Audet & Kwassi Joseph Dzahini & Michael Kokkolaras & Sébastien Le Digabel, 2021. "Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates," Computational Optimization and Applications, Springer, vol. 79(1), pages 1-34, May.
    4. Kwassi Joseph Dzahini, 2022. "Expected complexity analysis of stochastic direct-search," Computational Optimization and Applications, Springer, vol. 81(1), pages 179-200, January.
    5. Mohamed Gaha & Bilal Chabane & Dragan Komljenovic & Alain Côté & Claude Hébert & Olivier Blancke & Atieh Delavari & Georges Abdul-Nour, 2021. "Global Methodology for Electrical Utilities Maintenance Assessment Based on Risk-Informed Decision Making," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    6. Nicolau Andrés-Thió & Mario Andrés Muñoz & Kate Smith-Miles, 2022. "Bifidelity Surrogate Modelling: Showcasing the Need for New Test Instances," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3007-3022, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Rando & Cesare Molinari & Silvia Villa & Lorenzo Rosasco, 2024. "Stochastic zeroth order descent with structured directions," Computational Optimization and Applications, Springer, vol. 89(3), pages 691-727, December.
    2. Tommaso Giovannelli & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2022. "Derivative-free methods for mixed-integer nonsmooth constrained optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 293-327, June.
    3. Jiyuan Zhang & Bin Zhang & Shiqian Xu & Qihong Feng & Xianmin Zhang & Derek Elsworth, 2021. "Interpretation of Gas/Water Relative Permeability of Coal Using the Hybrid Bayesian-Assisted History Matching: New Insights," Energies, MDPI, vol. 14(3), pages 1-19, January.
    4. Moody Chu & Matthew Lin & Liqi Wang, 2014. "A study of singular spectrum analysis with global optimization techniques," Journal of Global Optimization, Springer, vol. 60(3), pages 551-574, November.
    5. Morgan Westbrook & William Rasdorf, 2023. "LED Traffic Signal Repair and Replacement Practices," Sustainability, MDPI, vol. 15(1), pages 1-18, January.
    6. W. L. Hare & Y. Lucet, 2014. "Derivative-Free Optimization Via Proximal Point Methods," Journal of Optimization Theory and Applications, Springer, vol. 160(1), pages 204-220, January.
    7. Youssef Diouane & Victor Picheny & Rodolophe Le Riche & Alexandre Scotto Di Perrotolo, 2023. "TREGO: a trust-region framework for efficient global optimization," Journal of Global Optimization, Springer, vol. 86(1), pages 1-23, May.
    8. Juliane Müller & Jangho Park & Reetik Sahu & Charuleka Varadharajan & Bhavna Arora & Boris Faybishenko & Deborah Agarwal, 2021. "Surrogate optimization of deep neural networks for groundwater predictions," Journal of Global Optimization, Springer, vol. 81(1), pages 203-231, September.
    9. Jean Bigeon & Sébastien Le Digabel & Ludovic Salomon, 2024. "Handling of constraints in multiobjective blackbox optimization," Computational Optimization and Applications, Springer, vol. 89(1), pages 69-113, September.
    10. 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.
    11. Youssef Diouane & Vyacheslav Kungurtsev & Francesco Rinaldi & Damiano Zeffiro, 2024. "Inexact direct-search methods for bilevel optimization problems," Computational Optimization and Applications, Springer, vol. 88(2), pages 469-490, June.
    12. J. Martínez & F. Sobral, 2013. "Constrained derivative-free optimization on thin domains," Journal of Global Optimization, Springer, vol. 56(3), pages 1217-1232, July.
    13. Belmiro P. M. Duarte & Anthony C. Atkinson & Satya P. Singh & Marco S. Reis, 2023. "Optimal design of experiments for hypothesis testing on ordered treatments via intersection-union tests," Statistical Papers, Springer, vol. 64(2), pages 587-615, April.
    14. Charles Audet & Andrew R. Conn & Sébastien Le Digabel & Mathilde Peyrega, 2018. "A progressive barrier derivative-free trust-region algorithm for constrained optimization," Computational Optimization and Applications, Springer, vol. 71(2), pages 307-329, November.
    15. Kwassi Joseph Dzahini, 2022. "Expected complexity analysis of stochastic direct-search," Computational Optimization and Applications, Springer, vol. 81(1), pages 179-200, January.
    16. Juliane Müller & Marcus Day, 2019. "Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 689-702, October.
    17. Andrea Brilli & Morteza Kimiaei & Giampaolo Liuzzi & Stefano Lucidi, 2024. "Worst Case Complexity Bounds for Linesearch-Type Derivative-Free Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 203(1), pages 419-454, October.
    18. Giovanni Stracquadanio & Elisa Pappalardo & Panos M. Pardalos, 2012. "A Mesh Adaptive Basin Hopping Method for the Design of Circular Antenna Arrays," Journal of Optimization Theory and Applications, Springer, vol. 155(3), pages 1008-1024, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:90:y:2025:i:3:d:10.1007_s10589-024-00645-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.