IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-1-4939-2483-7_10.html
   My bibliography  Save this book chapter

Swarm Intelligence in Optimal Management of Aquaculture Farms

In: Handbook of Operations Research in Agriculture and the Agri-Food Industry

Author

Listed:
  • A. Cobo

    (University of Cantabria)

  • I. Llorente

    (University of Cantabria)

  • L. Luna

    (University of Cantabria)

Abstract

Optimization techniques inspired by swarm intelligence (SI) have become increasingly popular during the last two decades. These techniques are based on the idea that groups of extremely simple agents with little or no organization can exhibit complex and intelligent behavior by using simple local rules and communication mechanisms. Thanks to this intelligent behavior, a group of social agents can carry out actions on a complex level and form decentralized and self-organizational systems. The advantage of these optimization approaches over traditional techniques is their robustness and flexibility, making SI especially appropriated to deal with complex optimization problems. In this chapter we introduce the concept of computational swarm intelligence; we present an overview of the most important optimization techniques inspired by swarm intelligence and examine the research contributions to the application of SI metaheuristics in different problems related to optimal management of aquaculture farms. As example of application we will present a particle swarm optimization (PSO) algorithm based on a bioeconomic model that helps managers of aquaculture enterprises in the process of decision-making, maximizing gross margin and minimizing operational risk.

Suggested Citation

  • A. Cobo & I. Llorente & L. Luna, 2015. "Swarm Intelligence in Optimal Management of Aquaculture Farms," International Series in Operations Research & Management Science, in: Lluis M. Plà-Aragonés (ed.), Handbook of Operations Research in Agriculture and the Agri-Food Industry, edition 127, chapter 0, pages 221-239, Springer.
    • Handle: RePEc:spr:isochp:978-1-4939-2483-7_10
    DOI: 10.1007/978-1-4939-2483-7_10
    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. Manuel Luna & Ignacio Llorente & Angel Cobo, 2022. "Determination of feeding strategies in aquaculture farms using a multiple-criteria approach and genetic algorithms," Annals of Operations Research, Springer, vol. 314(2), pages 551-576, July.

    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:978-1-4939-2483-7_10. 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.