IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/4465542.html
   My bibliography  Save this article

Mobile Robot Path Planning Based on Improved Coyote Optimization Algorithm

Author

Listed:
  • Jingyao Huang
  • Huihui Wu
  • Dimitris Mourtzis

Abstract

An improved coyote optimization algorithm (ICOA) is proposed to meet the requirements of global search capability, convergence speed, and stability for mobile robot path planning problems. Firstly, the population is divided into the elite group and the general groups after initialization, and the coyote individuals in the general groups are evolved by introducing the strategy of the best leading the poor. Secondly, after returning to the original position, coyotes adopt a new growth mode, and each individual makes a certain contribution to improve the level of the whole group. Thirdly, different from the original greedy algorithm, the survival of the fittest is carried out after each group of coyotes grows up, and the individuals do not affect each other. Finally, ICOA and the other seven optimization algorithms are simulated on four maps for mobile robot path planning. The simulation results show that ICOA can keep the diversity of the population, and has a strong global search ability, better stability, fast convergence speed, which reflects the strong optimization ability.

Suggested Citation

  • Jingyao Huang & Huihui Wu & Dimitris Mourtzis, 2022. "Mobile Robot Path Planning Based on Improved Coyote Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-14, June.
    • Handle: RePEc:hin:jnlmpe:4465542
    DOI: 10.1155/2022/4465542
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/4465542.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/4465542.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/4465542?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:hin:jnlmpe:4465542. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.