IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v86y2023i3d10.1007_s10898-022-01262-9.html
   My bibliography  Save this article

A robust multi-objective Bayesian optimization framework considering input uncertainty

Author

Listed:
  • Jixiang Qing

    (Ghent University – imec)

  • Ivo Couckuyt

    (Ghent University – imec)

  • Tom Dhaene

    (Ghent University – imec)

Abstract

Bayesian optimization is a popular tool for optimizing time-consuming objective functions with a limited number of function evaluations. In real-life applications like engineering design, the designer often wants to take multiple objectives as well as input uncertainty into account to find a set of robust solutions. While this is an active topic in single-objective Bayesian optimization, it is less investigated in the multi-objective case. We introduce a novel Bayesian optimization framework to perform multi-objective optimization considering input uncertainty. We propose a robust Gaussian Process model to infer the Bayes risk criterion to quantify robustness, and we develop a two-stage Bayesian optimization process to search for a robust Pareto frontier, i.e., solutions that have good average performance under input uncertainty. The complete framework supports various distributions of the input uncertainty and takes full advantage of parallel computing. We demonstrate the effectiveness of the framework through numerical benchmarks.

Suggested Citation

  • Jixiang Qing & Ivo Couckuyt & Tom Dhaene, 2023. "A robust multi-objective Bayesian optimization framework considering input uncertainty," Journal of Global Optimization, Springer, vol. 86(3), pages 693-711, July.
    • Handle: RePEc:spr:jglopt:v:86:y:2023:i:3:d:10.1007_s10898-022-01262-9
    DOI: 10.1007/s10898-022-01262-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-022-01262-9
    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/s10898-022-01262-9?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. Ivo Couckuyt & Dirk Deschrijver & Tom Dhaene, 2014. "Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization," Journal of Global Optimization, Springer, vol. 60(3), pages 575-594, November.
    2. Kaifeng Yang & Michael Emmerich & André Deutz & Thomas Bäck, 2019. "Efficient computation of expected hypervolume improvement using box decomposition algorithms," Journal of Global Optimization, Springer, vol. 75(1), pages 3-34, September.
    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. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Other publications TiSEM 8fa8d96f-a086-4c4b-88ab-9, Tilburg University, School of Economics and Management.
    2. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Balancing global and local search in parallel efficient global optimization algorithms," Journal of Global Optimization, Springer, vol. 67(4), pages 873-892, April.
    3. Jesús Martínez-Frutos & David Herrero-Pérez, 2016. "Kriging-based infill sampling criterion for constraint handling in multi-objective optimization," Journal of Global Optimization, Springer, vol. 64(1), pages 97-115, January.
    4. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    5. Jolan Wauters & Andy Keane & Joris Degroote, 2020. "Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization," Journal of Global Optimization, Springer, vol. 78(1), pages 137-160, September.
    6. Paul Feliot & Julien Bect & Emmanuel Vazquez, 2017. "A Bayesian approach to constrained single- and multi-objective optimization," Journal of Global Optimization, Springer, vol. 67(1), pages 97-133, January.
    7. Eichfelder, Gabriele & Warnow, Leo, 2023. "Advancements in the computation of enclosures for multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 310(1), pages 315-327.
    8. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Pseudo expected improvement criterion for parallel EGO algorithm," Journal of Global Optimization, Springer, vol. 68(3), pages 641-662, July.
    9. Prashant Singh & Ivo Couckuyt & Khairy Elsayed & Dirk Deschrijver & Tom Dhaene, 2017. "Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 172-193, October.
    10. Kaifeng Yang & Michael Emmerich & André Deutz & Thomas Bäck, 2019. "Efficient computation of expected hypervolume improvement using box decomposition algorithms," Journal of Global Optimization, Springer, vol. 75(1), pages 3-34, September.
    11. Nicolai Palm & Markus Landerer & Herbert Palm, 2022. "Gaussian Process Regression Based Multi-Objective Bayesian Optimization for Power System Design," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
    12. Lee, Juseong & Mitici, Mihaela, 2022. "Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    13. Duro, João A. & Ozturk, Umud Esat & Oara, Daniel C. & Salomon, Shaul & Lygoe, Robert J. & Burke, Richard & Purshouse, Robin C., 2023. "Methods for constrained optimization of expensive mixed-integer multi-objective problems, with application to an internal combustion engine design problem," European Journal of Operational Research, Elsevier, vol. 307(1), pages 421-446.
    14. Fuhao Ji & Auralee Edelen & Ryan Roussel & Xiaozhe Shen & Sara Miskovich & Stephen Weathersby & Duan Luo & Mianzhen Mo & Patrick Kramer & Christopher Mayes & Mohamed A. K. Othman & Emilio Nanni & Xiji, 2024. "Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction," Nature Communications, Nature, vol. 15(1), pages 1-7, 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:jglopt:v:86:y:2023:i:3:d:10.1007_s10898-022-01262-9. 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.