IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5036791.html
   My bibliography  Save this article

Legendre Cooperative PSO Strategies for Trajectory Optimization

Author

Listed:
  • Lei Liu
  • Yongji Wang
  • Fuqiang Xie
  • Jiashi Gao

Abstract

Particle swarm optimization (PSO) is a population-based stochastic optimization technique in a smooth search space. However, in a category of trajectory optimization problem with arbitrary final time and multiple control variables, the smoothness of variables cannot be satisfied since the linear interpolation is widely used. In the paper, a novel Legendre cooperative PSO (LCPSO) is proposed by introducing Legendre orthogonal polynomials instead of the linear interpolation. An additional control variable is introduced to transcribe the original optimal problem with arbitrary final time to the fixed one. Then, a practical fast one-dimensional interval search algorithm is designed to optimize the additional control variable. Furthermore, to improve the convergence and prevent explosion of the LCPSO, a theorem on how to determine the boundaries of the coefficient of polynomials is given and proven. Finally, in the numeral simulations, compared with the ordinary PSO and other typical intelligent optimization algorithms GA and DE, the proposed LCPSO has traits of lower dimension, faster speed of convergence, and higher accuracy, while providing smoother control variables.

Suggested Citation

  • Lei Liu & Yongji Wang & Fuqiang Xie & Jiashi Gao, 2018. "Legendre Cooperative PSO Strategies for Trajectory Optimization," Complexity, Hindawi, vol. 2018, pages 1-13, April.
  • Handle: RePEc:hin:complx:5036791
    DOI: 10.1155/2018/5036791
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/5036791.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2018/5036791.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2018/5036791?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
    ---><---

    Citations

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


    Cited by:

    1. Sheng Liu & Yuan Feng & Kang Shen & Yangqing Wang & Shengyong Chen, 2018. "An RGB-D-Based Cross-Field of View Pose Estimation System for a Free Flight Target in a Wind Tunnel," Complexity, Hindawi, vol. 2018, pages 1-9, December.

    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:complx:5036791. 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.