IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v204y2023icp498-528.html
   My bibliography  Save this article

An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems

Author

Listed:
  • Li, Yu
  • Yu, Xiaomei
  • Liu, Jingsen

Abstract

To solve complex high-dimensional optimization problems, an opposition-based butterfly optimization algorithm with adaptive elite mutation (OBOAEM) is proposed. In the initial stage, the opposition-based learning mechanism is introduced to increase the diversity of the initial population and improve the probability of finding the optimal value. In order to balance the process of global search and local search, the segmental adjustment factor is used to improve the optimization accuracy of the algorithm. In the final stage of the algorithm, the elite mutation strategy is adopted to prevent precocity of the algorithm. In this paper, 23 benchmark functions are selected to test OBOAEM and original algorithm BOA in low dimension, and 16 benchmark functions are introduced to test OBOAEM and eight intelligent optimization algorithms in high dimensions 100, 500 and 1000. In addition, the simulation experiments of 30 CEC2014 complex deformation functions reveal the OBOAEM has a good effect in solving complex optimization problems, which are compared with six state-of-art algorithms. Friedman test is used forstatistical analysis, showing that OBOAEM has better optimization performance for complex high-dimensional problems. Finally, OBOAEM is applied to engineering design problems, and it is proved that OBOAEM is competitive in solving real-world problems.

Suggested Citation

  • Li, Yu & Yu, Xiaomei & Liu, Jingsen, 2023. "An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 498-528.
    • Handle: RePEc:eee:matcom:v:204:y:2023:i:c:p:498-528
    DOI: 10.1016/j.matcom.2022.08.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475422003664
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2022.08.020?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feilong Chang & Fahui Yuan & Zhixiong Lu, 2023. "A Multi-Objective Optimization Method for a Tractor Driveline Based on the Diversity Preservation Strategy of Gradient Crowding," Agriculture, MDPI, vol. 13(7), pages 1-16, June.
    2. Ye, Wenwen & Li, Shengping, 2023. "Convergence analysis of flow direction algorithm in continuous search space and its improvement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 91-121.
    3. Pan, Jeng-Shyang & Zhang, Zhen & Chu, Shu-Chuan & Zhang, Si-Qi & Wu, Jimmy Ming-Tai, 2024. "A parallel compact Marine Predators Algorithm applied in time series prediction of Backpropagation neural network (BNN) and engineering optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 65-88.

    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:eee:matcom:v:204:y:2023:i:c:p:498-528. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.