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

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  • 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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.

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