IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v115y2002i3d10.1023_a1021207331209.html
   My bibliography  Save this article

Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors

Author

Listed:
  • Y. Liu

    (Ohio State University)

  • K.M. Passino

    (Ohio State University)

Abstract

In this paper, we explain the social foraging behavior of E. coli and M. xanthus bacteria and develop simulation models based on the principles of foraging theory that view foraging as optimization. This provides us with novel models of their foraging behavior and with new methods for distributed nongradient optimization. Moreover, we show that the models of both species of bacteria exhibit the property identified by Grunbaum that postulates that their foraging is social in order to be able to climb noisy gradients in nutrients. This provides a connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces.

Suggested Citation

  • Y. Liu & K.M. Passino, 2002. "Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 603-628, December.
    • Handle: RePEc:spr:joptap:v:115:y:2002:i:3:d:10.1023_a:1021207331209
    DOI: 10.1023/A:1021207331209
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1021207331209
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/A:1021207331209?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Hiba H. Darwish & Ayman Al-Quraan, 2023. "Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    2. Xu, Bin & Wu, Qi & Xi, Chen & He, Ren, 2020. "Recognition of the fatigue status of pilots using BF–PSO optimized multi-class GP classification with sEMG signals," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    3. Chuanjia Han & Bo Yang & Tao Bao & Tao Yu & Xiaoshun Zhang, 2017. "Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer," Energies, MDPI, vol. 10(5), pages 1-24, May.
    4. Panigrahi, B.K. & Ravikumar Pandi, V. & Das, Sanjoy & Das, Swagatam, 2010. "Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem," Energy, Elsevier, vol. 35(12), pages 4761-4770.
    5. Mehdi Zeynivand & Mehdi Najafi & Mohammad Modarres Yazdi, 2023. "A Recourse Policy to Improve Number of Successful Transplants in Uncertain Kidney Exchange Programs," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 476-507, May.

    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:joptap:v:115:y:2002:i:3:d:10.1023_a:1021207331209. 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.