IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v73y2019i3d10.1007_s10898-018-0715-1.html
   My bibliography  Save this article

Efficient global optimization of constrained mixed variable problems

Author

Listed:
  • Julien Pelamatti

    (ONERA
    Centre national d’études spatiales Direction des lanceurs)

  • Loïc Brevault

    (ONERA)

  • Mathieu Balesdent

    (ONERA)

  • El-Ghazali Talbi

    (Inria Lille - Nord Europe)

  • Yannick Guerin

    (Centre national d’études spatiales Direction des lanceurs)

Abstract

Due to the increasing demand for high performance and cost reduction within the framework of complex system design, numerical optimization of computationally costly problems is an increasingly popular topic in most engineering fields. In this paper, several variants of the Efficient Global Optimization algorithm for costly constrained problems depending simultaneously on continuous decision variables as well as on quantitative and/or qualitative discrete design parameters are proposed. The adaptation that is considered is based on a redefinition of the Gaussian Process kernel as a product between the standard continuous kernel and a second kernel representing the covariance between the discrete variable values. Several parameterizations of this discrete kernel, with their respective strengths and weaknesses, are discussed in this paper. The novel algorithms are tested on a number of analytical test-cases and an aerospace related design problem, and it is shown that they require fewer function evaluations in order to converge towards the neighborhoods of the problem optima when compared to more commonly used optimization algorithms.

Suggested Citation

  • Julien Pelamatti & Loïc Brevault & Mathieu Balesdent & El-Ghazali Talbi & Yannick Guerin, 2019. "Efficient global optimization of constrained mixed variable problems," Journal of Global Optimization, Springer, vol. 73(3), pages 583-613, March.
    • Handle: RePEc:spr:jglopt:v:73:y:2019:i:3:d:10.1007_s10898-018-0715-1
    DOI: 10.1007/s10898-018-0715-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-018-0715-1
    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/s10898-018-0715-1?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. Cédric Durantin & Julien Marzat & Mathieu Balesdent, 2016. "Analysis of multi-objective Kriging-based methods for constrained global optimization," Computational Optimization and Applications, Springer, vol. 63(3), pages 903-926, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jamie A. Manson & Thomas W. Chamberlain & Richard A. Bourne, 2021. "MVMOO: Mixed variable multi-objective optimisation," Journal of Global Optimization, Springer, vol. 80(4), pages 865-886, August.
    2. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.

    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. Kleijnen, Jack & van Nieuwenhuyse, I. & van Beers, W.C.M., 2022. "Constrained Optimization in Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (revision of 2021-031)," Other publications TiSEM 31a06a3b-dfc4-4431-a141-5, Tilburg University, School of Economics and Management.
    2. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    3. Shengguan Xu & Hongquan Chen, 2018. "Nash game based efficient global optimization for large-scale design problems," Journal of Global Optimization, Springer, vol. 71(2), pages 361-381, June.
    4. Angelina Roche, 2018. "Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety," Computational Statistics, Springer, vol. 33(1), pages 467-485, March.
    5. Yaohui Li & Jingfang Shen & Ziliang Cai & Yizhong Wu & Shuting Wang, 2021. "A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions," Mathematics, MDPI, vol. 9(2), pages 1-20, January.
    6. Lei, Fei & Gu, Ke & Du, Bin & Xie, Xiaoping, 2017. "Comprehensive global optimization of an implicit constrained multi-physics system for electric vehicles with in-wheel motors," Energy, Elsevier, vol. 139(C), pages 523-534.

    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:73:y:2019:i:3:d:10.1007_s10898-018-0715-1. 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.