Improved Bacterial Foraging Optimization with Social Cooperation and Adaptive Step Size

  • Xiaohui Yan
  • Yunlong Zhu
  • Hanning Chen
  • Hao Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)


This paper proposed an Improved Bacterial Foraging Optimization (IBFO) algorithm to enhance the optimization ability of original Bacterial Foraging Optimization. In the new algorithm, Social cooperation is introduced to guide the bacteria tumbling towards better directions. Meanwhile, adaptive step size is employed in chemotaxis process. The new algorithm is tested on a set of benchmark functions. Canonical BFO, Particle Swarm Optimization and Genetic Algorithm are employed for comparison. Experiment results show that the IBFO algorithm offers significant improvements over the original BFO algorithm and is a competitive optimizer for numerical optimization.


bacterial foraging optimization social cooperation adaptive search strategies 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaohui Yan
    • 1
    • 2
  • Yunlong Zhu
    • 1
  • Hanning Chen
    • 1
  • Hao Zhang
    • 1
    • 2
  1. 1.Key Laboratory of Industrial Informatics, Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina

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