IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-319-26024-2_11.html
   My bibliography  Save this book chapter

Worm Optimization for the Traveling Salesman Problem

In: Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling

Author

Listed:
  • Jean-Paul Arnaout

    (Gulf University for Science and Technology)

Abstract

In this research, a new metaheuristic called Worm Optimization (WO) is proposed, based on the foraging behaviors of Caenorhabditis elegans (Worms). In particular, the algorithm will mimic the behaviors of worms including finding food, avoiding toxins, interchanging between solitary and social foraging styles, alternating between food exploiting and seeking, and entering a stasis stage. WO effectiveness is illustrated on the traveling salesman problem (TSP), a known NP-hard problem, and compared to well-known naturally inspired algorithms using existing TSP data. The computational results reflected the superiority of WO in all tested problems. Furthermore, this superiority improved as problem sizes increased, and WO attained the global optimal solution in all tested problems within a reasonable computational time.

Suggested Citation

  • Jean-Paul Arnaout, 2016. "Worm Optimization for the Traveling Salesman Problem," International Series in Operations Research & Management Science, in: Ghaith Rabadi (ed.), Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, edition 1, chapter 0, pages 209-224, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-26024-2_11
    DOI: 10.1007/978-3-319-26024-2_11
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Jean-Paul Arnaout, 2020. "A worm optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times," Annals of Operations Research, Springer, vol. 285(1), pages 273-293, February.
    2. Jean-Paul Arnaout, 2018. "Worm optimization for the multiple level warehouse layout problem," Annals of Operations Research, Springer, vol. 269(1), pages 29-51, October.
    3. Jean-Paul Arnaout & John Khoury, 2022. "Adaptation of WO to the Euclidean location-allocation with unknown number of facilities," Annals of Operations Research, Springer, vol. 315(1), pages 57-72, August.

    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:spr:isochp:978-3-319-26024-2_11. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.