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MVMOO: Mixed variable multi-objective optimisation

Author

Listed:
  • Jamie A. Manson

    (University of Leeds)

  • Thomas W. Chamberlain

    (University of Leeds)

  • Richard A. Bourne

    (University of Leeds)

Abstract

In many real-world problems there is often the requirement to optimise multiple conflicting objectives in an efficient manner. In such problems there can be the requirement to optimise a mixture of continuous and discrete variables. Herein, we propose a new multi-objective algorithm capable of optimising both continuous and discrete bounded variables in an efficient manner. The algorithm utilises Gaussian processes as surrogates in combination with a novel distance metric based upon Gower similarity. The MVMOO algorithm was compared to an existing mixed variable implementation of NSGA-II and random sampling for three test problems. MVMOO shows competitive performance on all proposed problems with efficient data acquisition and approximation of the Pareto fronts for the selected test problems.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jglopt:v:80:y:2021:i:4:d:10.1007_s10898-021-01052-9
    DOI: 10.1007/s10898-021-01052-9
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    References listed on IDEAS

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    1. Fazlollahi, Samira & Mandel, Pierre & Becker, Gwenaelle & Maréchal, Francois, 2012. "Methods for multi-objective investment and operating optimization of complex energy systems," Energy, Elsevier, vol. 45(1), pages 12-22.
    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. 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.
    4. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    5. Eric Bradford & Artur M. Schweidtmann & Alexei Lapkin, 2018. "Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm," Journal of Global Optimization, Springer, vol. 71(2), pages 439-440, June.
    6. Eric Bradford & Artur M. Schweidtmann & Alexei Lapkin, 2018. "Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm," Journal of Global Optimization, Springer, vol. 71(2), pages 407-438, June.
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    Cited by:

    1. Thebelt, Alexander & Tsay, Calvin & Lee, Robert M. & Sudermann-Merx, Nathan & Walz, David & Tranter, Tom & Misener, Ruth, 2022. "Multi-objective constrained optimization for energy applications via tree ensembles," Applied Energy, Elsevier, vol. 306(PB).
    2. Patrick Rehill & Nicholas Biddle, 2022. "Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation," Papers 2212.06312, arXiv.org, revised Oct 2023.

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