Bacterial foraging-tabu search metaheuristics for identification of nonlinear friction model. (English) Zbl 1251.74026

Sarasiri, Nuapett; Suthamno, Kittiwong; Sujitjorn, Sarawut

J. Appl. Math. 2012, Article ID 238563, 23 p. (2012).

Summary: We propose new metaheuristic algorithms for an identification problem of nonlinear friction model. The proposed cooperative algorithms are formed from the bacterial foraging optimization (BFO) algorithm and the tabu search (TS). The paper reports the search comparison studies of the BFO, the TS, the genetic algorithm (GA), and the proposed metaheuristics. Search performances are assessed by using surface optimization problems. The proposed algorithms show superiority among them. A real-world identification problem of the Stribeck friction model parameters is presented. Experimental setup and results are elaborated.

MSC:

74P10	Optimization of other properties in solid mechanics
90C59	Approximation methods and heuristics in mathematical programming
74M10	Friction in solid mechanics

Keywords:

metaheuristic algorithms; identification problem; nonlinear friction; bacterial foraging optimization

Software:

Matlab; Genetic Algorithm and Direct Search Toolbox

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.
[2]	C. Onnen, R. Babu\vska, 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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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. · Zbl 05759100
[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. · Zbl 05848211
[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.
[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. · Zbl 1184.94170
[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. · Zbl 05739805
[16]	F. Glover, “Tabu search-part I,” ORSA Journal on Computing, vol. 1, pp. 190-206, 1989. · Zbl 0753.90054
[17]	F. Glover, “Tabu search-part II,” ORSA Journal on Computing, vol. 2, pp. 4-32, 1990. · Zbl 0771.90084
[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.
[19]	T. Kulworawanichpong and S. Sujitjorn, “Optimal power flow using tabu search,” IEEE Power Engineering Review, vol. 22, no. 6, pp. 37-40, 2002.
[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. · Zbl 0947.90590
[21]	R. Battiti and G. Tecchiolli, “The reactive tabu search,” ORSA Journal Computing, vol. 6, no. 2, pp. 126-140, 1994. · Zbl 0807.90094
[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. · Zbl 1020.90037
[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.
[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.
[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.
[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.
[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. · Zbl 1031.92038
[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. · Zbl 05452014
[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.
[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.
[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.
[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.
[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. · Zbl 05857106
[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.
[35]	E. Alba, Parallel Metaheuristics, Wiley-Interscience, New Jersey, NJ, USA, 2005. · Zbl 1094.90052
[36]	E. G. Talbi, Metaheuristics, John Wiley & Sons, New Jersey, NJ, USA, 2009. · Zbl 1176.90499
[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.
[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.
[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. · Zbl 0800.93424
[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. · Zbl 0821.93007
[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.