INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.7, No.8, Jul. 2017

Multiobjective Multipath Adaptive Tabu Search for Optimal PID Controller Design

Full Text (PDF, 391KB), PP.51-58


Views:99   Downloads:0

Author(s)

Deacha Puangdownreong

Index Terms

Multiobjective Multipath Adaptive Tabu Search, PID Controller, Metaheuristics, Control System Optimization

Abstract

The multipath adaptive tabu search (MATS) has been proposed as one of the most powerful metaheuristic optimization search techniques for solving the combinatorial and continuous optimization problems. The MATS employing the adaptive tabu search (ATS) as the search core has been proved and applied to various real-world engineering problems in single objective optimization manner. However, many design problems in engineering are typically multiobjective under complex nonlinear constraints. In this paper, the multiobjective multipath adaptive tabu search (mMATS) is proposed. The mMATS is validated against a set of multiobjective test functions, and then applied to design an optimal PID controller of the automatic voltage regulator (AVR) system. As results, the mMATS can provide very satisfactory solutions for all test functions as well as the control application.

Cite This Paper

Deacha Puangdownreong,"Multiobjective Multipath Adaptive Tabu Search for Optimal PID Controller Design", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.8, pp.51-58, 2015. DOI: 10.5815/ijisa.2015.08.07

Reference

[1]F. Glover and G. A. Kochenberger, Handbook of Metaheuristics, Kluwer Academic Publishers, Dordrecht, 2003.

[2]E. G. Talbi, Metaheuristics form Design to Implementation, John Wiley & Sons, Hoboken, 2009.

[3]D. T. Pham and D. Karaboga, Intelligent Optimisation Techniques, Springer, London, 2000.

[4]X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2010.

[5] X. S. Yang, “Review of metaheuristics and generalized evolutionary walk algorithm,” Int. J. Bio-Inspired Computation, vol.3(2), 2011, pp.77-84.

[6]B. Jarraya and A. Bouri, “Metaheuristic optimization backgrounds: a literature review,” Int. J. Contemporary Business Studies, vol.3(12), 2012, pp.31-44.

[7]F. Glover, “Tabu search - part I,” ORSA Journal on Computing, vol.1(3), 1989. pp.190-206.

[8]F. Glover, “Tabu search - part ii,” ORSA Journal on Computing, vol.2(1), 1990, pp.4-32.

[9]R. Battiti and G. Tecchiolli, “The reactive tabu search,” ORSA Journal on Computing, vol.6(2), 1994, pp.126-140.

[10]N.Wassan, “A reactive tabu search for the vehicle routing problem,” Journal of the Operational Research Society, vol.57(1), 2006, pp.111-116.

[11]T. Crainic, M. Toulouse, and M. Gendreau, “Toward a taxonomy of parallel Tabu search heuristics,” INFORMS Journal on Computing, vol.9(1), 1997, pp.61-72.

[12]E. G. Talbi, Z. Hafidi, and J.-M. Geib, “A parallel adaptive Tabu search approach,” Parallel Computing, vol. 24(14), 1998, pp.2003-2019.

[13]S. M. Sait, H. Youssef, H. R. Barada, and A. Y Ahmad, “Parallel Tabu search algorithm for VLSI standard-cell placement,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS ’00), 2000, pp.581-584.

[14]E. Nowicki and C. Smutnicki, “An advanced Tabu search algorithm for the job shop problem,” Journal of Scheduling, vol.8(2), 2005, pp.145-159.

[15]D. Zhang, Z. Fu, and L. Zhang, “An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems,” Electric Power Systems Research, vol.77(5-6), 2007, pp.685-694.

[16]F. Glover, “Parametric Tabu-search for mixed integer programs,” Computers and Operations Research, vol.33(9), 2006, pp.2449-2494.

[17]J. Xu, S. Y. Chiu, and F. Glover, “Probabilistic Tabu search for telecommunications network design,” Journal of Combinatorial Optimization, vol.1(1), 1997, pp.69-94.

[18]Y. Kochetov and E. Goncharov, “Behavior of a probabilistic tabu search algorithm for the multi stage uncapacitated facility location problem,” in Proc. Operations Research, 2000, pp.65-70.

[19]D. Ghosh, A Probabilistic Tabu Search Algorithm for the Generalized Minimum Spanning Tree Problem, Idian Institute of Management, Ahmedabad, India, 2003, Working Paper no. 2003-07-02.

[20]S. Sujitjorn, T. Kulworawanichpong, D. Puangdown-reong, and K.-N. Areerak, Adaptive Tabu Search and Applications in Engineering Design, Frontiers in Artificial Intelligent and Applications, IOS Press, Amsterdam, The Netherlands, 2006.

[21]D. Puangdownreong, K-N Areerak, A. Srikaew, S. Sujitjorn, and P. Totarong, “System identification via adaptive tabu search,” in Proc. IEEE Int. Conf. on Industrial Technology (ICIT’02), Bangkok, Thailand, vol. 2, 2002, pp.915-920.

[22]K.-N. Areerak, T. Kulworawanichpong, and S. Sujitjorn, “Moving towards a new era of intelligent protection through digital relaying in power system,” in Knowledge-Based Intelligent Information and Engineering Systems, vol. 3215 of Lecture Notes in Artificial Intelligence, pp. 1255–1261, Springer, New York, NY, USA, 2004.

[23]D. Puangdownreong and S. Sujitjorn, “Image approach to system identification,” WSEAS Transactions on Systems, vol.5(5), 2006, pp.930-938.

[24]D. Puangdownreong, K-N. Areerak, K-L. Areerak, T. Kulworawanichpong, and S. Sujitjorn, “Application of adaptive tabu search to system identification,” in Proc. the 24th IASTED Int. Conf. on Modelling, Identification, and Control (MIC2005), Innsbruck, Austria, 2005, pp.178-183.

[25]D. Puangdownreong and S. Sujitjorn, “Obtaining an optimum PID controller via adaptive tabu search,” Lecture Notes in Computer Science, vol. 4432(2), 2007, pp.747-755.

[26]C. Thammarat, D. Puangdownreong, P. Sukserm, and S. Suwannarongsri, “Optimum industrial PID controller design for process with time delay via adaptive tabu search,” in Proc. the 29th IASTED Int. Conf. on Modelling, Identification, and Control (MIC2010), Innsbruck, Austria, 2010, pp.197-202.

[27]N. Sriyingyong and K. Attakitmongcol, “Wavelet-based audio watermarking using adaptive tabu search,” in Proc. the 1st International Symposium on Wireless Pervasive Computing, 2006, pp.1-5.

[28]D. Puangdownreong, T. Kulworawanichpong, and S. Sujitjorn, “Finite convergence and performance evaluation of adaptive tabu search,” Lecture Notes in Computer Science, Springer-Verlag Heidelberg, vol. 3215, 2004, pp.710-717.

[29]D. Puangdownreong, S. Sujitjorn, and T. Kulwora-wanichpong, “Convergence analysis of adaptive tabu search,” ScienceAsia Journal of the Science Society of Thailand, vol.30(2), 2004, pp.183-190.

[30]J. Kluabwang, D. Puangdownreong, and S. Sujitjorn, “Management agent for search algorithms,” in Proc. the 12th WSEAS international conference on Computers, vol. 2(2), 2008, pp.375-382.

[31]J. Kluabwang, D. Puangdownreong, and S. Sujitjorn, “Management agent for search algorithms with surface optimization applications,” The WSEAS Trans. on Computers, vol.6(7), 2008, pp.791-803.

[32] D. Puangdownreong, J. Kluabwang, and S. Sujitjorn, “Multipath adaptive tabu search: its convergence and application to identification problem,” Journal of Physical Sciences, vol.7(33), 2012, pp.5288-5296.

[33]D. Puangdownreong, J. Kluabwang, and S. Sujitjorn, “Application of adaptive tabu search under management agent to control synthesis of scaled vehicle,” in Proc. the 29th IASTED Int. Conf. on Modelling, Identification, and Control (MIC2010), Innsbruck, Austria, 2010, pp.1-6.

[34]J. Kluabwang, D. Puangdownreong, and S. Sujitjorn, “Multipath adaptive tabu search for a vehicle control problem,” Journal of Applied Mathematics, vol.2012, 2012, pp.1-20.

[35]S. Sujitjorn, J. Kluabwang, D. Puangdownreong, and N. Sarasiri, “Adaptive tabu search and management agent,” The ECTI Trans. on Electrical Engineering, Electronics, and Communications, vol.7(2), 2009, pp.1-10.

[36]D-N. Le, “Improving genetic algorithm to solve multi-objectives optimal of upgrading infrastructure in NGWN,” International Journal of Intelligent Systems and Applications (IJISA), vol.5(12), 2013, pp.53-63.

[37]F. Y. Edgeworth, Mathematical Psychics: An Essay on the Application of Mathematics to the Moral Sciences, C. Kegan Paul and Co., London, 1881.

[38]V. Pareto, Cours ď économie Politique, Rouge, Lausanne, Switzerland, 1896.

[39]C. Yunfang, “A general framework for multi-objective optimization immune algorithms,” International Journal of Intelligent Systems and Applications (IJISA), vol.4(6), 2012, pp.1-13.

[40]E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol.(3), 1999, pp.257-271.

[41]E. Zitzler, K. Deb, and L. Thiele, “Comparison of multiobjective evolutionary algorithms: empirical results,” Evolution Computing, vol.8, 2000, pp.173-195.

[42]J. D. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in Proc. the 1st International Conference on Genetic Algorithms, 1985, pp.93-100.

[43]K. Deb, A. Pratap, S. Agarwal, and T. Mayarivan, “A fast and elitist multiobjective algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol.6, 2002, pp.182-197.

[44]T. Robicˇ, B. Filipicˇ, “DEMO: differential evolution for multiobjective optimization,” Lecture Notes in Computer Sciences, vol. 3410, 2005, pp.520-533.

[45]Z. L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system,” IEEE Transactions on Energy Conversion, vol.19(2), 2004, pp. 384-391.