IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v15y2021i4p1-26.html
   My bibliography  Save this article

An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem

Author

Listed:
  • Anbang Wang

    (Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China)

  • Lihong Guo

    (Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China)

  • Yuan Chen

    (Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China)

  • Junjie Wang

    (Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China)

  • Luo Liu

    (Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China)

  • Yuanzhang Song

    (Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China)

Abstract

The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.

Suggested Citation

  • Anbang Wang & Lihong Guo & Yuan Chen & Junjie Wang & Luo Liu & Yuanzhang Song, 2021. "An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(4), pages 1-26, October.
    • Handle: RePEc:igg:jcini0:v:15:y:2021:i:4:p:1-26
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.20211001.oa17
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.

    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:igg:jcini0:v:15:y:2021:i:4:p:1-26. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.