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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    2. 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.
    3. Liu, Jialin & Jiang, Rui & Liu, Yang & Jia, Bin & Li, Xingang & Wang, Ting, 2024. "Managing evacuation of multiclass traffic flow: Fleet configuration, lane allocation, lane reversal, and cross elimination," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).

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