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Foundations of Computational Intelligence Volume 3 pp 23–55Cite as

Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications

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Part of the Studies in Computational Intelligence book series (SCI,volume 203)

Abstract

Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. The underlying biology behind the foraging strategy of E.coli is emulated in an extraordinary manner and used as a simple optimization algorithm. This chapter starts with a lucid outline of the classical BFOA. It then analyses the dynamics of the simulated chemotaxis step in BFOA with the help of a simple mathematical model. Taking a cue from the analysis, it presents a new adaptive variant of BFOA, where the chemotactic step size is adjusted on the run according to the current fitness of a virtual bacterium. Nest, an analysis of the dynamics of reproduction operator in BFOA is also discussed. The chapter discusses the hybridization of BFOA with other optimization techniques and also provides an account of most of the significant applications of BFOA until date.

Keywords

  • Particle Swarm Optimization
  • Differential Evolution
  • Independent Component Analysis
  • Fitness Landscape
  • Unit Step Function

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-01085-9_2
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Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India

    Swagatam Das, Arijit Biswas & Sambarta Dasgupta

  2. Norwegian University of Science and Technology, Norway

    Ajith Abraham

Authors
  1. Swagatam Das
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  2. Arijit Biswas
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  3. Sambarta Dasgupta
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  4. Ajith Abraham
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Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Université Paris XII Fac. Sciences, LERISS, 61 avenue du Général de Gaulle Building P2 - Room 350, 94010, Créteil, France

    Patrick Siarry

  4. Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    Andries Engelbrecht

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Das, S., Biswas, A., Dasgupta, S., Abraham, A. (2009). Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_2

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  • DOI: https://doi.org/10.1007/978-3-642-01085-9_2

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