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Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 698057, 28 pages
http://dx.doi.org/10.1155/2012/698057
Research Article

Bacterial Colony Optimization

Ben Niu1,2 and Hong Wang1

1College of Management, Shenzhen University, Shenzhen 518060, China
2Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China

Received 27 May 2012; Accepted 24 August 2012

Academic Editor: Binggen Zhang

Copyright © 2012 Ben Niu and Hong Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Q. Tan, Q. He, W. Zhao, Z. Shi, and E. S. Lee, “An improved FCMBP fuzzy clustering method based on evolutionary programming,” Computers & Mathematics with Applications, vol. 61, no. 4, pp. 1129–1144, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  2. J. A. Vasconcelos, J. A. Ramírez, R. H. C. Takahashi, and R. R. Saldanha, “Improvements in genetic algorithms,” IEEE Transactions on Magnetics, vol. 37, no. 5, pp. 3414–3417, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Akbari and K. Ziarati, “A multilevel evolutionary algorithm for optimizing numerical functions,” International Journal of Industrial Engineering Computations, vol. 2, no. 2, pp. 419–430, 2011.
  4. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995. View at Scopus
  5. J. Kennedy and R. C. Eberhart, Swarm Intelligence, Morgan Kaufmann, San Francisco, Calif, USA, 2001.
  6. M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization artificial ants as a computational intelligence technique,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Dorigo and C. Blum, “Ant colony optimization theory: a survey,” Theoretical Computer Science, vol. 344, no. 2-3, pp. 243–278, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  8. X. L. Li, Z. J. Shao, and J. X. Qian, “Optimizing method based on autonomous animats: fish-swarm Algorithm,” System Engineering Theory and Practice, vol. 22, no. 11, pp. 32–38, 2002. View at Scopus
  9. D. Karaboga and B. Akay, “A comparative study of artificial Bee colony algorithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  10. D. Karaboga and B. Akay, “A survey: algorithms simulating bee swarm intelligence,” Artificial Intelligence Review, vol. 31, no. 1–4, pp. 61–85, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, vol. 22, no. 3, pp. 52–67, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. S. D. Müller, J. Marchetto, S. Airaghi, and P. Koumoutsakos, “Optimization based on bacterial chemotaxis,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 16–29, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Das, S. Dasgupta, A. Biswas, A. Abraham, and A. Konar, “On stability of the chemotactic dynamics in bacterial-foraging optimization algorithm,” IEEE Transactions on Systems, Man, and Cybernetics Part A, vol. 39, no. 3, pp. 670–679, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. M. S. Li, T. Y. Ji, W. J. Tang, Q. H. Wu, and J. R. Saunders, “Bacterial foraging algorithm with varying population,” BioSystems, vol. 100, no. 3, pp. 185–197, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Dasgupta, A. Biswas, A. Abraham, and S. Das, “Adaptive computational chemotaxis in bacterial foraging algorithm,” in Proceedings of the 2nd International Conference on Complex, Intelligent and Software Intensive Systems (CISIS '08), pp. 64–71, March 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Chu, H. Mi, H. Liao, Z. Ji, and Q. H. Wu, “A Fast Bacterial Swarming Algorithm for high-dimensional function optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '08), pp. 3135–3140, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. D. H. Kim, “Hybrid GA-BF based intelligent PID controller tuning for AVR system,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 11–22, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. H. N. Chen, Y. L. Zhu, and K. Y. Hu, “Adaptive bacterial foraging algorithm,” Abstract and Applied Analysis, vol. 2011, Article ID 108269, 27 pages, 2011. View at Publisher · View at Google Scholar
  19. B. Niu, Y. Fan, H. Wang, L. Li, and X. Wang, “Novel bacterial foraging optimization with time-varying chemotaxis step,” International Journal of Artificial Intelligence, vol. 7, no. 11, pp. 257–273, 2011.
  20. B. Niu, H. Wang, L. J. Tan, and L. Li, “Improved BFO with adaptive chemotaxis step for global optimization,” in Proceedings of International Conference on Computational Intelligence and Security (CIS '11), pp. 76–80, 2011.
  21. X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82–102, 1999. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Salomon, “Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms,” BioSystems, vol. 39, no. 3, pp. 263–278, 1996. View at Publisher · View at Google Scholar · View at Scopus
  23. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, Boston, Mass, USA, 1989.