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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 494271, 7 pages
http://dx.doi.org/10.1155/2014/494271
Research Article

Novel Adaptive Bacteria Foraging Algorithms for Global Optimization

Department of Automatic Control & Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK

Received 5 August 2013; Revised 11 February 2014; Accepted 20 February 2014; Published 25 March 2014

Academic Editor: Cheng-Jian Lin

Copyright © 2014 Ahmad N. K. Nasir et al. 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. 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
  2. S. Mishra, “A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 1, pp. 61–73, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Supriyono and M. O. Tokhi, “Adaptation schemes of chemotactic step size of bacterial foraging algorithm for faster convergence,” Journal of Artificial Intelligence, vol. 4, no. 4, pp. 207–219, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Venkaiah and D. M. Vinod Kumar, “Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power re-scheduling of generators,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 4921–4930, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Majhi, G. Panda, B. Majhi, and G. Sahoo, “Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques,” Expert Systems with Applications, vol. 36, no. 6, pp. 10097–10104, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Dasgupta, S. Das, A. Abraham, and A. Biswas, “Adaptive computational chemotaxis in bacterial foraging optimization: an analysis,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 4, pp. 919–941, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Chen and W. Lin, “An improved bacterial foraging optimization,” in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '09), pp. 2057–2062, Guangxi, China, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. I. A. Farhat and M. E. El-Hawary, “Modified bacterial foraging algorithm for optimum economic dispatch,” in Proceedings of the IEEE Electrical Power and Energy Conference (EPEC '09), pp. 1–6, Montreal Quebec, Canada, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Huang and W. Lin, “Parameter estimation of Wiener model based on improved bacterial foraging optimization,” in Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence (AICI '10), pp. 174–178, Sanya, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. 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, Hong Kong, China, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Niu, Y. Fan, P. Zhao, B. Xue, L. Li, and Y. Chai, “A novel bacterial foraging optimizer with linear decreasing chemotaxis step,” in Proceedings of the 2nd International Workshop on Intelligent Systems and Applications (ISA '10), pp. 1–4, Wuhan, China, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. X. Yan, Y. Zhu, H. Chen, and H. Zhang, Improved Bacterial Foraging Optimization with Social Cooperation and Adaptive Step Size, Springer, Berlin, Germany, 2012.
  13. X. Xu, Y. H. Liu, A. M. Wang, G. Wang, and H. L. Chen, “A new adaptive bacterial swarm algorithm,” in Proceedings of the 8th International Conference on Natural Computing (ICNC '12), pp. 991–995, Chongqing, China, May 2012.
  14. S. F. Toha, S. Julai, and M. O. Tokhi, “Ant colony based model prediction of a twin rotor system,” Procedia Engineering, vol. 41, pp. 1135–1144, 2012.