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Journal of Applied Mathematics
Volume 2012, Article ID 238563, 23 pages
http://dx.doi.org/10.1155/2012/238563
Research Article

Bacterial Foraging-Tabu Search Metaheuristics for Identification of Nonlinear Friction Model

Power Electronics, Machines, and Control Research Group, Control and Automation Research Unit, School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Received 20 February 2012; Accepted 28 April 2012

Academic Editor: Hak-Keung Lam

Copyright © 2012 Nuapett Sarasiri 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. T. Watanabe, Y. Hashimoto, I. Nishikawa, and H. Tokumaru, “Line balancing using a genetic evolution model,” Control Engineering Practice, vol. 3, no. 1, pp. 69–76, 1995. View at Google Scholar · View at Scopus
  2. C. Onnen, R. Babuška, U. Kaymak, J. M. Sousa, H. B. Verbruggen, and R. Isermann, “Genetic algorithms for optimization in predictive control,” Control Engineering Practice, vol. 5, no. 10, pp. 1363–1372, 1997. View at Publisher · View at Google Scholar · View at Scopus
  3. E. W. McGookin and D. J. Murray-Smith, “Submarine manoeuvring controller's optimisation using simulated annealing and genetic algorithms,” Control Engineering Practice, vol. 14, no. 1, pp. 1–15, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Marinaki, Y. Marinakis, and G. E. Stavroulakis, “Fuzzy control optimized by PSO for vibration suppression of beams,” Control Engineering Practice, vol. 18, no. 6, pp. 618–629, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. J. G. Gray, D. J. Murray-Smith, Y. Li, K. C. Sharman, and T. Weinbrenner, “Nonlinear model structure identification using genetic programming,” Control Engineering Practice, vol. 6, no. 11, pp. 1341–1352, 1998. View at Google Scholar · View at Scopus
  6. B. Abdelhadi, A. Benoudjit, and N. Nait-Said, “Application of genetic algorithm with a novel adaptive scheme for the identification of induction machine parameters,” IEEE Transactions on Energy Conversion, vol. 20, no. 2, pp. 284–291, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Alonge, F. D'Ippolito, and F. M. Raimondi, “Least squares and genetic algorithms for parameter identification of induction motors,” Control Engineering Practice, vol. 9, no. 6, pp. 647–657, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Zheng and P. Wang, “Parameter structure identification using tabu search and simulated annealing,” Advances in Water Resources, vol. 19, no. 4, pp. 215–224, 1996. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Kulworawanichpong, K.-L Areerak, K.-N Areerak, and S. Sujitjorn, “Harmonic identification for active power filters via adaptive tabu search method,” Lecture Notes in Computer Science, vol. 3215, part 3, pp. 687–694, 2004. View at Google Scholar · View at Scopus
  10. Y. Liu and X. He, “Modeling identification of power plant thermal process based on PSO algorithm,” in Proceedings of the American Control Conference (ACC 05), pp. 4484–4489, Portland, Ore, USA, June 2005. View at Scopus
  11. J. Meier, W. Schaedler, L. Borgatti, A. Corsini, and T. Schanz, “Inverse parameter identification technique using PSO algorithm applied to geotechnical modeling,” Journal of Artificial Evolution and Applications, vol. 2008, Article ID 574613, 14 pages, 2008. View at Publisher · View at Google Scholar
  12. V. Khanagha, A. Khanagha, and V. T. Vakili, “Modified particle swarm optimization for blind deconvolution and identification of multi channel FIR Filters,” Eurasip Journal on Advances in Signal Processing, vol. 2008, Article ID 280635, 6 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. X. He and J. J. Liu, “Aquifer parameter identification with ant colony optimization algorithm,” in Proceedings of the International Workshop on Intelligent Systems and Applications (ISA '09), pp. 1–4, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Liu, M. Fukumoto, S. Saiki, and S. Zhang, “A variable step-size proportionate affine projection algorithm for identification of sparse impulse response,” Eurasip Journal on Advances in Signal Processing, vol. 2009, Article ID 150914, 10 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Chen, Y. Zhu, and K. Hu, “Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning,” Applied Soft Computing Journal, vol. 10, no. 2, pp. 539–547, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. F. Glover, “Tabu search—part I,” ORSA Journal on Computing, vol. 1, pp. 190–206, 1989. View at Google Scholar
  17. F. Glover, “Tabu search—part II,” ORSA Journal on Computing, vol. 2, pp. 4–32, 1990. View at Google Scholar
  18. G. Zhang, W. Habenicht, and W. E. L. Spieß, “Improving the structure of deep frozen and chilled food chain with tabu search procedure,” Journal of Food Engineering, vol. 60, no. 1, pp. 67–79, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Kulworawanichpong and S. Sujitjorn, “Optimal power flow using tabu search,” IEEE Power Engineering Review, vol. 22, no. 6, pp. 37–40, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. E. Nowicki and C. Smutnicki, “A fast tabu search algorithm for the permutation flow-shop problem,” European Journal of Operational Research, vol. 91, no. 1, pp. 160–175, 1996. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Battiti and G. Tecchiolli, “The reactive tabu search,” ORSA Journal Computing, vol. 6, no. 2, pp. 126–140, 1994. View at Google Scholar
  22. Y. A. Kochetov and E. N. Goncharov, “Probabilistic tabu search algorithm for the multi-stage uncapacitated facility location problem,” in Operations Research Proceedings, pp. 65–70, Springer, 2001. View at Google Scholar · View at Zentralblatt MATH
  23. S. Sujitjorn, T. Kulworawanichpong, D. Puangdownreong, and K.-N. Areerak, “Adaptive tabu search and applications in engineering design,” in Integrated Intelligent Systems for Engineering Design, X. F. Zha and R. J. Howlett, Eds., pp. 233–257, IOS Press, Amsterdam, The Netherlands, 2006. View at Google Scholar
  24. S. Sujitjorn and S. Khwan-on, “Learning control via neuro-tabu-fuzzy controller,” Lecture Notes in Computer Science, vol. 4251, pp. 833–840, 2006. View at Google Scholar · View at Scopus
  25. N. Sriyingyong and K. Attakitmongcol, “Wavelet-based audio watermarking using adaptive tabu search,” in Proceedings of the 1st International Symposium on Wireless Pervasive Computing, pp. 1–5, January 2006. View at Scopus
  26. 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
  27. Y. Liu and K. M. Passino, “Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors,” Journal of Optimization Theory and Applications, vol. 115, no. 3, pp. 603–628, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  28. 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
  29. S. Mishra and C. N. Bhende, “Bacterial foraging technique-based optimized active power filter for load compensation,” IEEE Transactions on Power Delivery, vol. 22, no. 1, pp. 457–465, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Tripathy, S. Mishra, L. L. Lai, and Q. P. Zhang, “Transmission loss reduction based on FACTS and bacteria foraging algorithm,” Lecture Notes in Computer Science, vol. 4193, pp. 222–231, 2006. View at Google Scholar · View at Scopus
  31. W. J. Tang, M. S. Li, Q. H. Wu, and J. R. Saunders, “Bacterial foraging algorithm for optimal power flow in dynamic environments,” IEEE Transactions on Circuits and Systems, vol. 55, no. 8, pp. 2433–2442, 2008. View at Publisher · View at Google Scholar
  32. T. Datta, I. S. Misra, B. B. Mangaraj, and S. Imtiaj, “Improved adaptive bacteria foraging algorithm in optimization of antenna array for faster convergence,” Progress in Electromagnetics Research, vol. 1, pp. 143–157, 2008. View at Google Scholar
  33. 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
  34. C. Blum and A. Roli, “Metaheuristics in combinatorial optimization: overview and conceptual comparison,” ACM Computing Surveys, vol. 35, no. 3, pp. 268–308, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. E. Alba, Parallel Metaheuristics, Wiley-Interscience, New Jersey, NJ, USA, 2005. View at Publisher · View at Google Scholar
  36. E. G. Talbi, Metaheuristics, John Wiley & Sons, New Jersey, NJ, USA, 2009.
  37. J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, 1975.
  38. J. H. Holland, “Genetic algorithms,” Scientific American, vol. 267, no. 1, pp. 66–72, 1992. View at Google Scholar · View at Scopus
  39. MathWorks, “Genetic Algorithm and Direct Search Toolbox: for Use with MATLAB,” User’s Guide, Version 1, MathWorks, Natick, Mass, USA, 2005.
  40. B. Armstrong-Hélouvry, “Stick slip and control in low-speed motion,” Institute of Electrical and Electronics Engineers. Transactions on Automatic Control, vol. 38, no. 10, pp. 1483–1496, 1993. View at Publisher · View at Google Scholar
  41. B. Armstrong-Helouvry, P. Dupont, and C. Cadudas de Wit, “A survey of model, analysis tools and compensation methods for the control of machines with friction,” Automatica, vol. 30, no. 7, pp. 1083–1138, 1994. View at Google Scholar
  42. C. Canudas de Wit, H. Olsson, K. J. Åström, and P. Lischinsky, “A new model for control of systems with friction,” Institute of Electrical and Electronics Engineers. Transactions on Automatic Control, vol. 40, no. 3, pp. 419–425, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  43. H. Du and S. S. Nair, “Modeling and compensation of low-velocity friction with bounds,” IEEE Transactions on Control Systems Technology, vol. 7, no. 1, pp. 110–121, 1999. View at Google Scholar · View at Scopus