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Journal of Applied Mathematics
Volume 2012, Article ID 147950, 20 pages
http://dx.doi.org/10.1155/2012/147950
Research Article

Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

1Department of Applied Mathematics, Xidian University, Xi’an 710071, China
2School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China
3School of Science, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
4School of Science, Guilin University of Technology, Guilin 541004, China

Received 13 February 2012; Revised 24 April 2012; Accepted 18 May 2012

Academic Editor: Yuri Sotskov

Copyright © 2012 Longquan Yong 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. Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001. View at Google Scholar · View at Scopus
  2. K. S. Lee and Z. W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice,” Computer Methods in Applied Mechanics and Engineering, vol. 194, no. 36-38, pp. 3902–3933, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. G. G. Roy, P. Chakraborty, and S. Das, “Designing fractional-order PIλDμ controller using differential harmony search algorithm,” International Journal of Bioinspired Computing, vol. 2, no. 5, pp. 303–309, 2010. View at Google Scholar
  4. B. K. Panigrahi, V. R. Pandi, S. Das, and Z. Cui, “Dynamic economic load dispatch with wind energy using modified harmony search,” International Journal of Bio-Inspired Computing, vol. 2, no. 3-4, pp. 282–289, 2010. View at Google Scholar
  5. V. R. Pandi, B. K. Panigrahi, R. C. Bansal, S. Das, and A. Mohapatra, “Economic load dispatch using hybrid swarm intelligence based harmony search algorithm,” Electric Power Components and Systems, vol. 39, no. 8, pp. 751–767, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. B. K. Panigrahi, V. R. Pandi, S. Das, and A. Abraham, “A bandwidth-adaptive harmony search algorithm to solve optimal power flow problems with non-smooth cost functions,” in Recent Advances in Harmony Search Algorithm, Z. W. Geem, Ed., Studies in Computational Intelligence, pp. 65–75, Springer, 2010. View at Google Scholar
  7. S. Kulluk, L. Ozbakir, and A. Baykasoglu, “Training neural networks with harmony search algorithms for classification problems,” Engineering Applications of Artificial Intelligence, vol. 25, no. 1, pp. 11–19, 2012. View at Publisher · View at Google Scholar
  8. T. K. Gandhi, P. Chakraborty, G. G. Roy, and B. K. Panigrahi, “Discrete harmony search based expert model for epileptic seizure detection in electroencephalography,” Expert Systems with Applications, vol. 39, no. 4, pp. 4055–4062, 2012. View at Publisher · View at Google Scholar
  9. I. Ahmad, M. G. Mohammad, A. A. Salman, and S. A. Hamdan, “Broadcast scheduling in packet radio networks using Harmony Search algorithm,” Expert Systems with Applications, vol. 39, no. 1, pp. 1526–1535, 2012. View at Publisher · View at Google Scholar
  10. M. A. Al-Betar, A. T. Khader, and M. Zaman, “University course timetabling using a hybrid harmony search metaheuristic algorithm,” IEEE Transactions on Systems, Man and Cybernetics Part C, vol. 42, no. 5, pp. 664–681, 2012. View at Publisher · View at Google Scholar
  11. A. Kaveh and M. Ahangaran, “Discrete cost optimization of composite floor system using social harmony search model,” Applied Soft Computing, vol. 12, no. 1, pp. 372–381, 2012. View at Google Scholar
  12. M. G. H. Omran and M. Mahdavi, “Global-best harmony search,” Applied Mathematics and Computation, vol. 198, no. 2, pp. 643–656, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. P. Chakraborty, G. G. Roy, S. Das, D. Jain, and A. Abraham, “An improved harmony search algorithm with differential mutation operator,” Fundamenta Informaticae, vol. 95, no. 4, pp. 401–426, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. X. Z. Gao, X. Wang, and S. J. Ovaska, “Uni-modal and multi-modal optimization using modified Harmony Search methods,” International Journal of Innovative Computing, Information and Control, vol. 5, no. 10, pp. 2985–2996, 2009. View at Google Scholar · View at Scopus
  15. C. M. Wang and Y. F. Huang, “Self-adaptive harmony search algorithm for optimization,” Expert Systems with Applications, vol. 37, no. 4, pp. 2826–2837, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Q. K. Pan, P. N. Suganthan, M. F. Tasgetiren, and J. J. Liang, “A self-adaptive global best harmony search algorithm for continuous optimization problems,” Applied Mathematics and Computation, vol. 216, no. 3, pp. 830–848, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Zou, L. Gao, J. Wu, and S. Li, “Novel global harmony search algorithm for unconstrained problems,” Neurocomputing, vol. 73, no. 16–18, pp. 3308–3318, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. O. M. Alia and R. Mandava, “The variants of the harmony search algorithm: an overview,” Artificial Intelligence Review, vol. 36, no. 1, pp. 49–68, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Das, A. Mukhopadhyay, A. Roy, A. Abraham, and B. K. Panigrahi, “Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 41, no. 1, pp. 89–106, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Das and P. N. Suganthan, “Differential evolution: a survey of the state-of-the-art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Ga Mperle, S. D. Muller, and P. Koumoutsakos, “A parameter study for differential evolution,” in Proceedings of the WSEAS International Conference on Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298, 2002.
  22. P. N. Suganthan, N. Hansen, J. Liang et al., “Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization,” Tech. Rep. KanGAL 2005005, Nanyang Technological University, Singapore, IITKanpur, India, 2005. View at Google Scholar
  23. http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO_files/Page364.htm.