ICA  Vol.4 No.1 , February 2013
Current Search: Performance Evaluation and Application to DC Motor Speed Control System Design
ABSTRACT

This paper proposes the current search (CS) metaheuristics conceptualized from the electric current flowing through electric networks for optimization problems with continuous design variables. The CS algorithm possesses two powerful strategies, exploration and exploitation, for searching the global optimum. Based on the stochastic process, the derivatives of the objective function is unnecessary for the proposed CS. To evaluate its performance, the CS is tested against several unconstrained optimization problems. The results obtained are compared to those obtained by the popular search techniques, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO), and the adaptive tabu search (ATS). As results, the CS outperforms other algorithms and provides superior results. The CS is also applied to a constrained design of the optimum PID controller for the dc motor speed control system. From experimental results, the CS has been successfully applied to the speed control of the dc motor.


Cite this paper
D. Puangdownreong, "Current Search: Performance Evaluation and Application to DC Motor Speed Control System Design," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 42-54. doi: 10.4236/ica.2013.41007.
References
[1]   F. Glover and G. A. Kochenberger, “Handbook of Metaheuristics,” Kluwer Academic Publishers, Dordrecht, 2003.

[2]   E. G. Talbi, “Metaheuristics Forn Design to Implementation,” John Wiley & Sons, Hoboken, 2009. doi:10.1002/9780470496916

[3]   X. S. Yang, “Nature-Inspired Metaheuristic Algorithms,” Luniver Press, 2010.

[4]   D. E. Goldberg, “Genetic Algorithms in Search Optimization and Machine Learning,” Addison Wesley Publishers, Boston, 1989.

[5]   MathWorks, “Genetic Algorithm and Direct Search Toolbox: For Use with MATLAB,” User’s Guide, Version 1, MathWorks, Natick, Mass, 2005.

[6]   M. Dorigo and T. Stützle, “Ant Colony Optimization,” MIT Press, Cambridge, 2004. doi:10.1007/b99492

[7]   J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” IEEE Proceedings of the International Conference on Neural Networks, Vol. 4, 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968

[8]   Z. W. Geem, “Recent Advance in Harmony Search Algorithm,” Studies in Computational Intelligence, Springer, Berlin, 2010. doi:10.1007/978-3-642-04317-8

[9]   X. S. Yang, “Firefly Algorithms for Multimodal Optimization, Stochastic Algorithms,” Foundations and Applications SAGA 2009, Lecture Notes in Computer Sciences, Vol. 5792, 2009, pp. 169-178. doi:10.1007/978-3-642-04944-6_14

[10]   R. Oftadeh, M. J. Mahjoob and M. Shariatpanahi, “A Novel Mata-Heuristic Optimization Algorithm Inspired by Group Hunting of Animals: Hunting Search,” Computers and Mathematics with Applications, Vol. 60, No. 7, 2010, pp. 2087-2098. doi:10.1016/j.camwa.2010.07.049

[11]   X. S. Yang and S. Deb, “Cuckoo Search via Lévy Flights,” In: Proceedings of the World Congress on Nature and Biologically Inspired Computing, IEEE Publications, 2009, pp. 210-214.

[12]   X. S. Yang and S. Deb, “Engineering Optimization by Cuckoo Search,” International Journal of Mathematical Modeling and Numerical Optimization, Vol. 1, No. 4, 2010, pp. 330-343. doi:10.1504/IJMMNO.2010.035430

[13]   X. S. Yang, “A New Metaheuristic Bat-Inspired Algorithm,” In: J. R. Gonzalez, et al., Eds., Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), Springer, Berlin, 2010, pp. 65-74.

[14]   S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, Vol. 220, No. 4598, 1983, pp. 671-680. doi:10.1126/science.220.4598.671

[15]   P. J. M. van Laarhoven and E. H. L. Aarts, “Simulated Annealing: Theory and Applications,” Kluwer Academic Publishers, Dordrecht, 1987. doi:10.1007/978-94-015-7744-1

[16]   F. Glover, “Tabu Search—Part I,” ORSA Journal on Computing, Vol. 1, No. 3, 1989, pp. 190-206.

[17]   F. Glover, “Tabu Search—Part II,” ORSA Journal on Computing, Vol. 2, No. 1, 1990, pp. 4-32. doi:10.1287/ijoc.2.1.4

[18]   S. Sujitjorn, T. Kulworawanichpong, D. Puangdownreong and K.-N. Areerak, “Adaptive Tabu Search and Applications in Engineering Design,” Frontiers in Artificial Intelligent and Applications, IOS Press, Amsterdam, 2006.

[19]   D. Puangdownreong, T. Kulworawanichpong and S. Sujitjorn, “Finite Convergence and Performance Evaluation of Adaptive Tabu Search,” In: Lecture Notes in Artificial Intelligence, Springer-Verlag, Heidelberg, 2004, pp. 710-717.

[20]   A. Sukulin and D. Puangdownreong, “A Novel MetaHeuristic Optimization Algorithm: Current Search,” Proceedings of the 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED ‘12), Cambridge, 2012, pp. 125-130.

[21]   D. Puangdownreong and A. Sukulin, “Obtaining an Optimum PID Controllers for Unstable Systems using Current Search,” International Journal of Systems Engineering, Applications & Development, Vol. 2, No. 6, 2012, pp. 188-195.

[22]   D. Puangdownreong, “Application of Current Search to Optimum PIDA Controller Design,” Intelligent Control and Automation, Vol. 3, No. 4, 2012, pp. 303-312.

[23]   M. M. Ali, C. Khompatraporn and Z. B. Zabinsky, “A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems,” Journal of Global Optimization, Vol. 31, No. 4, 2005, pp. 635-672. doi:10.1007/s10898-004-9972-2

[24]   K. Ogata, “Modern Control Engineering,” Prentice Hall, New Jersey, 2010.

[25]   MathWorks, “System Identification Toolbox,” User’s Guide, Version 7.2, 2008

 
 
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