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Bacterial Foraging Optimization

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  • Kevin M. Passino

    (The Ohio State University, USA)

Abstract

The bacterial foraging optimization (BFO) algorithm mimics how bacteria forage over a landscape of nutrients to perform parallel nongradient optimization. In this article, the author provides a tutorial on BFO, including an overview of the biology of bacterial foraging and the pseudo-code that models this process. The algorithms features are briefly compared to those in genetic algorithms, other bio-inspired methods, and nongradient optimization. The applications and future directions of BFO are also presented.

Suggested Citation

  • Kevin M. Passino, 2010. "Bacterial Foraging Optimization," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(1), pages 1-16, January.
    • Handle: RePEc:igg:jsir00:v:1:y:2010:i:1:p:1-16
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jsir.2010010101
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    Cited by:

    1. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    2. Yingying Liao & Weiguo Zhao & Liying Wang, 2021. "Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers," Mathematics, MDPI, vol. 9(18), pages 1-38, September.
    3. Behera, Sasmita & Sahoo, Subhrajit & Pati, B.B., 2015. "A review on optimization algorithms and application to wind energy integration to grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 214-227.
    4. Surafel Luleseged Tilahun & Mohamed A. Tawhid, 2019. "Swarm hyperheuristic framework," Journal of Heuristics, Springer, vol. 25(4), pages 809-836, October.
    5. Shuang Wang & Abdelazim G. Hussien & Heming Jia & Laith Abualigah & Rong Zheng, 2022. "Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(10), pages 1-32, May.

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