IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p3867-d779070.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/3867/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/3867/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. Aimee van Wynsberghe & Tijs Vandemeulebroucke & Larissa Bolte & Jamila Nachid, 2022. "Special Issue “Towards the Sustainability of AI; Multi-Disciplinary Approaches to Investigate the Hidden Costs of AI”," Sustainability, MDPI, vol. 14(24), pages 1-4, December.
    2. Yaming Guo & Ke Zhang & Xiqun Chen & Meng Li, 2023. "Proactive Coordination of Traffic Guidance and Signal Control for a Divergent Network," Mathematics, MDPI, vol. 11(20), pages 1-19, October.

    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. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    2. Xi Liu & Yugang He & Renhong Wu, 2024. "Revolutionizing Environmental Sustainability: The Role of Renewable Energy Consumption and Environmental Technologies in OECD Countries," Energies, MDPI, vol. 17(2), pages 1-21, January.
    3. Elzbieta Rynska & Joanna Klimowicz & Slawomir Kowal & Krzysztof Lyzwa & Michal Pierzchalski & Wojciech Rekosz, 2020. "Smart Energy Solutions as an Indispensable Multi-Criteria Input for a Coherent Urban Planning and Building Design Process—Two Case Studies for Smart Office Buildings in Warsaw Downtown Area," Energies, MDPI, vol. 13(15), pages 1-24, July.
    4. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    5. Stéphanie Camaréna, 2021. "Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    6. Keeheon Lee, 2021. "A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    7. Dolores Gallardo-Vázquez, 2023. "Attributes influencing responsible tourism consumer choices: Sustainable local food and drink, health-related services, and entertainment," Oeconomia Copernicana, Institute of Economic Research, vol. 14(2), pages 645-686, June.
    8. Gianluca MISURACA & Colin van Noordt, 2020. "AI Watch - Artificial Intelligence in public services: Overview of the use and impact of AI in public services in the EU," JRC Research Reports JRC120399, Joint Research Centre.
    9. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(C).
    10. Jaros³aw Brodny & Magdalena Tutak, 2023. "The level of implementing sustainable development goal "Industry, innovation and infrastructure" of Agenda 2030 in the European Union countries: Application of MCDM methods," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 47-102, March.
    11. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    12. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Sergio Genovesi & Julia Maria Mönig, 2022. "Acknowledging Sustainability in the Framework of Ethical Certification for AI," Sustainability, MDPI, vol. 14(7), pages 1-10, March.
    14. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    15. Wang, Weilong & Xiao, Deheng & Wang, Jianlong & Wu, Haitao, 2024. "The cost of pollution in the digital era: Impediments of air pollution on enterprise digital transformation," Energy Economics, Elsevier, vol. 134(C).
    16. Kim, Myung Ja & Hall, C. Michael & Kwon, Ohbyung & Sohn, Kwonsang, 2024. "Space tourism: Value-attitude-behavior theory, artificial intelligence, and sustainability," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    17. Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.
    18. Qian, Yu & Xu, Zeshui & Qin, Yong & Gou, Xunjie & Skare, Marinko, 2023. "Measuring the varying relationships between sustainable development and oil booms in different contexts: An empirical study," Resources Policy, Elsevier, vol. 85(PB).
    19. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
    20. Hanna Obracht-Prondzyńska & Ewa Duda & Helena Anacka & Jolanta Kowal, 2022. "Greencoin as an AI-Based Solution Shaping Climate Awareness," IJERPH, MDPI, vol. 19(18), pages 1-25, September.

    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:gam:jsusta:v:14:y:2022:i:7:p:3867-:d:779070. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.