Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 905712, 11 pages
http://dx.doi.org/10.1155/2014/905712
Research Article

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

1Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, Shanghai 200237, China
2Shanghai Dianji University, Shanghai 201306, China

Received 4 December 2013; Accepted 6 February 2014; Published 16 March 2014

Academic Editor: Huaicheng Yan

Copyright © 2014 Weitian Lin 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. R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43, Nagoya, Japan, October 1995. View at Scopus
  2. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, December 1995. View at Scopus
  3. Y. Shi and R. C. Eberhart, “A modified particle swarms optimiser,” in Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308, 1997.
  4. Y. Shi and R. Eberhart, “Modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '98), pp. 69–73, May 1998. View at Scopus
  5. H. Zhang, H. Yan, F. Yang, and Q. Chen, “Quantized control design for impulsive fuzzy networked systems,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 6, pp. 1153–1162, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the Congress on Evolutionary Computation (CEC '02), pp. 1671–1676, IEEE Press, 2002.
  7. 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, May 1998. View at Scopus
  8. M. Lovbjerg, T. K. Rasmussen, and T. Krink, “Hybrid particle swarm optimization with breeding and subpopulations,” in Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 1217–1222, San Diego, Calif, USA, 2000.
  9. B. Jiao, Z. Lian, and X. Gu, “A dynamic inertia weight particle swarm optimization algorithm,” Chaos, Solitons and Fractals, vol. 37, no. 3, pp. 698–705, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  10. 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
  11. H. Zhang, H. Yan, F. Yang, and Q. Chen, “Distributed average filtering for sensor networks with sensor saturation,” IET Control Theory & Applications, vol. 7, no. 6, pp. 887–893, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  12. R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” in Proceedings of IEEE International Conference on Evolutionary Computation, pp. 81–86, May 2001. View at Scopus
  13. Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimization,” in Evolutionary Programming VII: Proceedings of the 7th Annual Conference on Evolutionary Programming, pp. 591–600, Springer, New York, NY, USA, 1998. View at Google Scholar
  14. F. van den Bergh and A. P. Engelbecht, “Cooperative learning in neural networks using particle swarm optimizers,” South African Computer Journal, vol. 26, pp. 84–90, 2000. View at Google Scholar
  15. H. C. Yan, Z. Z. Su, H. Zhang, and F. W. Yang, “Observer-based H control for discrete-time stochastic systems with quantisation and random communication delays,” IET Control Theory & Applications, vol. 7, no. 3, pp. 372–379, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  16. M. A. Abido, “Optimal power flow using particle swarm optimization,” International Journal of Electrical Power and Energy Systems, vol. 24, no. 7, pp. 563–571, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Salman, I. Ahmad, and S. Al-Madani, “Particle swarm optimization for task assignment problem,” Microprocessors and Microsystems, vol. 26, no. 8, pp. 363–371, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, “A Particle swarm optimization for reactive power and voltage control considering voltage security assessment,” IEEE Transactions on Power Systems, vol. 15, no. 4, pp. 1232–1239, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. M. M. El-Sherbiny and R. M. Alhamali, “A hybrid particle swarm algorithm with artificial immune learning for solving the fixed charge transportation problem,” Computers & Industrial Engineering, vol. 64, pp. 610–620, 2013. View at Publisher · View at Google Scholar
  20. H. Zhang, H. Yan, T. Liu, and Q. Chen, “Fuzzy controller design for nonlinear impulsive fuzzy systems with time delay,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 5, pp. 844–856, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. N. Hamta, S. M. T. Fatemi Ghomi, F. Jolai, and M. Akbarpour Shirazi, “A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect,” International Journal of Production Economics, vol. 141, no. 1, pp. 99–111, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. F. D. Chou, “Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks,” International Journal of Production Economics, vol. 141, pp. 137–145, 2013. View at Google Scholar