Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 587820, 12 pages
http://dx.doi.org/10.1155/2014/587820
Research Article

A Cooperative Harmony Search Algorithm for Function Optimization

College of Automation, Harbin Engineering University, Harbin 150001, China

Received 12 December 2013; Accepted 26 January 2014; Published 6 March 2014

Academic Editor: Rongni Yang

Copyright © 2014 Gang Li and Qingzhong Wang. 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. 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
  2. J. H. Holland, Adaptation in Natural and Artificial Systems, 1975. View at MathSciNet
  3. D. B. Fogel, L. J. Fogel, and J. W. Atmar, “Meta-evolutionary programming,” in Proceedings of the 25th Asilomar Conference on Signals, Systems & Computers, pp. 540–545, IEEE, November 1991. View at Scopus
  4. I. Rechenberg, Cybernetic Solution Path of an Experimental Problem, 1965.
  5. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Wash, USA, December 1995. View at Scopus
  6. M. Dorigo and L. M. Gambardella, “Ant colonies for the travelling salesman problem,” BioSystems, vol. 43, no. 2, pp. 73–81, 1997. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Kirkpatrick, C. D. Gelatt Jr., and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  8. 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
  9. F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to participle swam optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225–239, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. K. A. De Jong and M. A. Potter, “Evolving complex structures via cooperative coevolution,” in Evolutionary Programming, pp. 307–317, 1995. View at Google Scholar
  11. M. A. Potter and K. A. De Jong, “A cooperative coevolutionary approach to function optimization,” in Parallel Problem Solving from Nature—PPSN III, vol. 866, pp. 249–257, Springer, 1994. View at Google Scholar
  12. X.-S. Yang, “Harmony search as a metaheuristic algorithm,” in Music-Inspired Harmony Search Algorithm, vol. 191 of Studies in Computational Intelligence, pp. 1–14, Springer, 2009. View at Publisher · View at Google Scholar
  13. Z. W. Geem, “Optimal cost design of water distribution networks using harmony search,” Engineering Optimization, vol. 38, no. 3, pp. 259–277, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. Z. W. Geem, J. H. Kim, and G. V. Loganathan, “Harmony search optimization: application to pipe network design,” International Journal of Modelling and Simulation, vol. 22, no. 2, pp. 125–133, 2002. View at Google Scholar · View at Scopus
  15. D.-X. Zou, L.-Q. Gao, P.-F. Wu, and J.-H. Wu, “A global harmony search algorithm and its application to PID control,” Journal of Northeastern University, vol. 31, no. 11, pp. 1534–1537, 2010. View at Google Scholar · View at Scopus
  16. M. Mahdavi and H. Abolhassani, “Harmony K-means algorithm for document clustering,” Data Mining and Knowledge Discovery, vol. 18, no. 3, pp. 370–391, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. L. Wang, R. Yang, Y. Xu, Q. Niu, P. M. Pardalos, and M. Fei, “An improved adaptive binary harmony search algorithm,” Information Sciences, vol. 232, pp. 58–87, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  18. M. Mahdavi, M. Fesanghary, and E. Damangir, “An improved harmony search algorithm for solving optimization problems,” Applied Mathematics and Computation, vol. 188, no. 2, pp. 1567–1579, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  19. 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 Zentralblatt MATH · View at MathSciNet · View at Scopus
  20. A. P. Engelbrecht, Computational Intelligence: An Introduction, John Wiley & Sons, 2007.
  21. Z. Yang, K. Tang, and X. Yao, “Large scale evolutionary optimization using cooperative coevolution,” Information Sciences, vol. 178, no. 15, pp. 2985–2999, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. W. Chen, T. Weise, Z. Yang, and K. Tang, “Large-scale global optimization using cooperative coevolution with variable interaction learning,” in Parallel Problem Solving from Nature, PPSN XI, vol. 6239 of Lecture Notes in Computer Science, pp. 300–309, Springer, 2010. View at Google Scholar