About this Journal Submit a Manuscript Table of Contents
Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 791373, 21 pages
http://dx.doi.org/10.1155/2012/791373
Research Article

A Novel PSO Model Based on Simulating Human Social Communication Behavior

Yanmin Liu1,2 and Ben Niu3,4,5

1School of Economics and Management, Tongji University, Shanghai 200092, China
2School of Mathematics and Computer Science, Zunyi Normal College, Zunyi 563002, China
3College of Management, Shenzhen University, Shenzhen 518060, China
4Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
5E-Business Technology Institute, The University of Hong Kong, Hong Kong

Received 11 May 2012; Revised 22 June 2012; Accepted 25 June 2012

Academic Editor: Vimal Singh

Copyright © 2012 Yanmin Liu and Ben Niu. 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. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, December 1995. View at Scopus
  2. R. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in Proceedings of the 7th Annual Conference on Evolutionary Programming, San Diego, Calif, USA, 1998.
  3. M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the IEEE Congress on Evolution Computation, pp. 1671–1676, Honolulu, Hawaii, USA, 2002.
  5. J. Sun, J. Liu, and W. Xu, “Using quantum-behaved particle swarm optimization algorithm to solve non-linear programming problems,” International Journal of Computer Mathematics, vol. 84, no. 2, pp. 261–272, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. P. J. Angeline, “Using selection to improve particle swarm optimization,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC'98), pp. 84–89, Anchorage, Alaska, USA, May 1998. View at Scopus
  7. M. Lovbjerg and T. Krink, “Hybrid particle swarm optimizer with breeding and subpopulations,” in Proceeding of the Genetic Evolution Computation Conference, pp. 469–476, 2001.
  8. A. Stacey and M. Jancic, “Particle swarm optimization with mutation,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1425–1430, Canberra, Australia, 2003.
  9. P. S. Andrews, “An investigation into mutation operators for particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC'06), pp. 1044–1051, July 2006. View at Scopus
  10. P. N. Suganthan, “Particle swarm optimizer with neighborhood operator,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1958–1962, Washington, DC, USA, 1999.
  11. X. Hu and R. C. Eberhart, “Multiobjective optimization using dynamic neighborhood particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1677–1681, Honolulu, Hawaii, USA, 2002.
  12. J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Peram and K. Veeramachaneni, “Fitness-distance-ratio based particle swarm optimization,” in Proceeding of the IEEE Swarm Intelligence Symposium, pp. 174–181, April 2003.
  14. A. S. Mohais, R. Mendes, C. Ward, and C. Posthoff, “Neighborhood re-structuring in particle swarm optimization,” in AI 2005: Advances in Artificial Intelligence, vol. 3809 of Lecture Notes in Computer Science, pp. 776–785, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  15. S. Janson and M. Middendorf, “A hierarchical particle swarm optimizer and its adaptive variant,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 35, no. 6, pp. 1272–1282, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Elshamy, H. M. Emara, and A. Bahgat, “Clubs-based particle swarm optimization,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS'07), pp. 289–296, Honolulu, Hawaii, USA, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: simpler, maybe better,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 204–210, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. 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
  19. S. Z. Zhao, J. J. Liang, P. N. Suganthan, and M. F. Tasgetiren, “Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC'08), pp. 3845–3852, Hong Kong, China, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. M. Liu, Q. Z. Zhao, C. L. Sui, and Z. Z. Shao, “Particle swarm optimizer based on dynamic neighborhood topology and mutation operator,” Control and Decision, vol. 25, no. 7, pp. 968–974, 2010. View at Scopus
  21. R. Salomon, “Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms,” BioSystems, vol. 39, no. 3, pp. 263–278, 1996. View at Publisher · View at Google Scholar · View at Scopus