IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v91y2025i3d10.1007_s10898-024-01442-9.html
   My bibliography  Save this article

Adaptive nested Monte Carlo approach for multi-objective efficient global optimization

Author

Listed:
  • Shengguan Xu

    (Nanjing Tech University)

  • Jianfeng Tan

    (Nanjing Tech University)

  • Jiale Zhang

    (Xiamen University)

  • Hongquan Chen

    (Nanjing University of Aeronautics and Astronautics)

  • Yisheng Gao

    (Nanjing University of Aeronautics and Astronautics)

Abstract

This paper presents a novel algorithm, namely the adaptive nested Monte Carlo based multi-objective Efficient Global Optimization (ANMC-MOEGO), which aims to enhance efficiency and accuracy while minimizing programming complexity in contrast to traditional multi-objective Efficient Global Optimization (MOEGO). In this algorithm, the programming complexity is streamlined by employing Monte Carlo simulation for both hypervolume improvement (HVI) and expected hypervolume improvement (EHVI) calculations. Furthermore, the efficiency and accuracy of HVI and EHVI calculations are improved through the utilization of a novel technique called adaptive Monte Carlo hypercube boundaries (AMCHB), which is based on the bisection method. The algorithm is validated via a set of test functions from the open literature. The numerical results demonstrate that the ANMC-MOEGO algorithm produces solutions closer to the theoretical results, with improved distributions on the corresponding Pareto fronts compared to the algorithm without AMCHB technique. Moreover, when obtaining a better Pareto front, the proposed algorithm is found to be more time-efficient, achieving speedups of up to 22.57 times.

Suggested Citation

  • Shengguan Xu & Jianfeng Tan & Jiale Zhang & Hongquan Chen & Yisheng Gao, 2025. "Adaptive nested Monte Carlo approach for multi-objective efficient global optimization," Journal of Global Optimization, Springer, vol. 91(3), pages 647-676, March.
    • Handle: RePEc:spr:jglopt:v:91:y:2025:i:3:d:10.1007_s10898-024-01442-9
    DOI: 10.1007/s10898-024-01442-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-024-01442-9
    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/s10898-024-01442-9?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.

    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:91:y:2025:i:3:d:10.1007_s10898-024-01442-9. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.