Synergy of PSO and Bacterial Foraging Optimization — A Comparative Study on Numerical Benchmarks

  • Arijit Biswas
  • Sambarta Dasgupta
  • Swagatam Das
  • Ajith Abraham
Part of the Advances in Soft Computing book series (AINSC, volume 44)


Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. Until now, very little research work has been undertaken to improve the convergence speed and accuracy of the basic BFOA over multi-modal fitness landscapes. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFOA algorithm for optimizing multi-modal and high dimensional functions. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm, the classical g_best PSO algorithm and a state of the art version of the PSO. The new method is shown to be statistically significantly better on a five-function test-bed and one difficult engineering optimization problem of spread spectrum radar poly-phase code design.


Bacterial Foraging hybrid optimization particle swarm optimization Radar poly-phase code design 


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  1. 1.
    Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control, IEEE Control Systems Magazine, 52–67, (2002).Google Scholar
  2. 2.
    Mishra, S.: A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. IEEE Trans. on Evolutionary Computation, vol. 9(1): 61–73, (2005).CrossRefGoogle Scholar
  3. 3.
    Tripathy, M., Mishra, S., Lai, L.L. and Zhang, Q.P.: Transmission Loss Reduction Based on FACTS and Bacteria Foraging Algorithm. PPSN, 222–231, (2006).Google Scholar
  4. 4.
    Kim, D.H., Cho, C. H.: Bacterial Foraging Based Neural Network Fuzzy Learning. IICAI 2005, 2030–2036.Google Scholar
  5. 5.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Harbor (1975).Google Scholar
  6. 6.
    Kennedy, J, Eberhart, R.: Particle swarm optimization, In Proceedings of IEEE International Conference on Neural Networks, (1995) 1942–1948.Google Scholar
  7. 7.
    Storn, R., Price, K.: Differential evolution — A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization, 11(4) 341–359, (1997).zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Kim, D.H., Abraham, A., Cho, J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization, Information Sciences, Vol. 177(18), 3918–3937, (2007).CrossRefGoogle Scholar
  9. 9.
    Mladenovic, P., Kovacevic-Vuijcic, C.: Solving spread-spectrum radar polyphase code design problem by tabu search and variable neighborhood search, European Journal of Operational Research, 153(2003) 389–399.CrossRefGoogle Scholar
  10. 10.
    Stephens, D.W., Krebs, J.R., Foraging Theory, Princeton University Press, Princeton, New Jersey, (1986).Google Scholar
  11. 11.
    Yao, X., Liu, Y., Lin, G. Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation, vol 3, No 2, 82–102, (1999).CrossRefGoogle Scholar
  12. 12.
    Angeline, P. J.: Evolutionary optimization versus particle swarm optimization: Philosophy and the performance difference, Lecture Notes in Computer Science (vol. 1447), Proceedings of 7th International Conference on. Evolutionary Programming-Evolutionary Programming VII (1998) 84–89.Google Scholar
  13. 13.
    Ratnaweera, A., Halgamuge, K.S.: Self organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, In IEEE Transactions on Evolutionary Computation 8(3): 240–254, (2004).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Arijit Biswas
    • 1
  • Sambarta Dasgupta
    • 1
  • Swagatam Das
    • 1
  • Ajith Abraham
    • 2
  1. 1.Dept. of Electronics and Telecommunication EnggJadavpur UniversityKolkataIndia
  2. 2.Norwegian University of Science and TechnologyNorway

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