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A Survey on Sustainable Surrogate-Based Optimisation

Author

Listed:
  • Laurens Bliek

    (School of Industrial Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
    Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands)

Abstract

Surrogate-based optimisation (SBO) algorithms are a powerful technique that combine machine learning and optimisation to solve expensive optimisation problems. This type of problem appears when dealing with computationally expensive simulators or algorithms. By approximating the expensive part of the optimisation problem with a surrogate, the number of expensive function evaluations can be reduced. This paper defines sustainable SBO, which consists of three aspects: applying SBO to a sustainable application, reducing the number of expensive function evaluations, and considering the computational effort of the machine learning and optimisation parts of SBO. The paper reviews sustainable applications that have successfully applied SBO over the past years, and analyses the used framework, type of surrogate used, sustainable SBO aspects, and open questions. This leads to recommendations for researchers working on sustainability-related applications who want to apply SBO, as well as recommendations for SBO researchers. It is argued that transparency of the computation resources used in the SBO framework, as well as developing SBO techniques that can deal with a large number of variables and objectives, can lead to more sustainable SBO.

Suggested Citation

  • Laurens Bliek, 2022. "A Survey on Sustainable Surrogate-Based Optimisation," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3867-:d:779070
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    References listed on IDEAS

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    1. 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.
    2. Motahareh Saadatpour, 2020. "An Adaptive Surrogate Assisted CE-QUAL-W2 Model Embedded in Hybrid NSGA-II_ AMOSA Algorithm for Reservoir Water Quality and Quantity Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1437-1451, March.
    3. Ali Sadollah & Mohammad Nasir & Zong Woo Geem, 2020. "Sustainability and Optimization: From Conceptual Fundamentals to Applications," Sustainability, MDPI, vol. 12(5), pages 1-34, March.
    4. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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