IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0336402.html
   My bibliography  Save this article

The application of intelligent optimization algorithms in reducing energy consumption in slipform construction

Author

Listed:
  • Wenqin Wang
  • Lijun Li

Abstract

In this work, a new Hybrid PSO-Whale Optimization (HPWO) algorithm is introduced to optimize energy consumption in slipform construction. By combining the global exploration power of Particle Swarm Optimization (PSO) with the local exploitation strengths of Whale Optimization Algorithm (WOA), the HPWO algorithm enhances energy management through dynamic adjustment mechanisms. A comprehensive multi-objective optimization model is developed, addressing the interactions between hydraulic, climbing, and vibration systems. Experimental results demonstrate that the HPWO algorithm reduces energy consumption by an average of 18.5%, outperforming traditional optimization methods and offering a practical solution for improving construction efficiency.

Suggested Citation

  • Wenqin Wang & Lijun Li, 2025. "The application of intelligent optimization algorithms in reducing energy consumption in slipform construction," PLOS ONE, Public Library of Science, vol. 20(11), pages 1-18, November.
  • Handle: RePEc:plo:pone00:0336402
    DOI: 10.1371/journal.pone.0336402
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0336402
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0336402&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0336402?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0336402. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.