IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-19-9733-4_7.html
   My bibliography  Save this book chapter

Structure of Salp Swarm Algorithm

In: Application of Machine Learning Models in Agricultural and Meteorological Sciences

Author

Listed:
  • Mohammad Ehteram

    (Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering)

  • Akram Seifi

    (Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture)

  • Fatemeh Barzegari Banadkooki

    (Payame Noor University, Agricultural Department)

Abstract

This chapter explains the theory of the salp swarm algorithm (SSA). The SSA can be easily implemented. Also, adjusting SSA parameters is easy. The fast convergence and high accuracy are the advantages of SSA. The SSA can be coupled with optimization algorithms to solve complex problems. SSA is an example of a strong algorithm. This algorithm has few parameters. The best solution in the algorithm will guide the other solutions. The SSA can be coupled with soft computing models for finding their parameter values.

Suggested Citation

  • Mohammad Ehteram & Akram Seifi & Fatemeh Barzegari Banadkooki, 2023. "Structure of Salp Swarm Algorithm," Springer Books, in: Application of Machine Learning Models in Agricultural and Meteorological Sciences, chapter 0, pages 61-65, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9733-4_7
    DOI: 10.1007/978-981-19-9733-4_7
    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
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:spr:sprchp:978-981-19-9733-4_7. 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.