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)

Abstract

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.

Keywords

bacterial foraging optimization social cooperation adaptive search strategies 

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References

  1. 1.
    Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control. IEEE Control Systems Magazine 22, 52–67 (2002)CrossRefGoogle Scholar
  2. 2.
    Chen, H., Zhu, Y., Hu, K.: Cooperative Bacterial Foraging Optimization. Discrete Dynamics in Nature and Society, Article ID 815247, 17 pages (2009)Google Scholar
  3. 3.
    Wu, C., Zhang, N., Jiang, J., Yang, J., Liang, Y.: Improved Bacterial Foraging Algorithms and Their Applications to Job Shop Scheduling Problems. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007, Part I. LNCS, vol. 4431, pp. 562–569. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Majhi, R., Panda, G., Majhi, B., Sahoo, G.: Efficient Prediction of Stock Market Indices Using Adaptive Bacterial Foraging Optimization (ABFO) and BFO Based Techniques. Expert Systems with Applications 36(6), 10097–10104 (2009)CrossRefGoogle Scholar
  5. 5.
    Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann (2001)Google Scholar
  6. 6.
    Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway, pp. 303–308 (1998)Google Scholar
  7. 7.
    Chen, H., Zhu, Y., Hu, K.: Self-Adaptation in Bacterial Foraging Optimization Algorithm. In: Proceedings of the 3rd International Conference on Intelligent System & Knowledge Engineering, Xiamen, China, pp. 1026–1031 (2008)Google Scholar
  8. 8.
    Zhou, B., Gao, L., Dai, Y.: Gradient Methods with Adaptive Step-Sizes. Computational Optimization and Applications 35(1), 69–86 (2006)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Karaboga, D., Basturk, B.: A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization 39(3), 459–471 (2007)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Zou, W., Zhu, Y., Chen, H., Sui, X.: A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm. Discrete Dynamics in Nature and Society, Article ID 459796, 16 pages (2010)Google Scholar
  11. 11.
    Yan, X., Zhu, Y., Zou, W.: A Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization. In: 11th International Conference on Hybrid Intelligent Systems, pp. 127–132 (2011)Google Scholar
  12. 12.
    Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Transcations on Evolutionary Computing 10, 281–295 (2006)CrossRefGoogle Scholar
  13. 13.
    Shi, Y., Eberhart, R.C.: Empirical Study of Particle Swarm Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway, NJ, USA, vol. 3, pp. 1945–1950 (1999)Google Scholar

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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