Advertisement

Numerical Optimization Using Organizational Particle Swarm Algorithm

  • Lin Cong
  • Yuheng Sha
  • Licheng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

The classical particle swarm optimization (PSO) has its own disadvantages, such as low convergence speed and prematurity. All of these make solutions have probability to convergence to local optimizations. In order to overcome the disadvantages of PSO, an organizational particle swarm algorithm (OPSA) is presented in this paper. In OPSA, the initial organization is a set of particles. By competition and cooperation between organizations in every generation, particles can adapt the environment better, and the algorithm can converge to global optimizations. In experiments, OPSA is tested on 6 unconstrained benchmark problems, and the experiment results are compared with PSO_TVIW, MPSO_TVAC, HPSO_TVAC and FEP. The results indicate that OPSA performs much better than other algorithms both in quality of solutions and in computational complexity. Finally, the relationship between parameters and success ratio are analyzed.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway, NJ (1995)CrossRefGoogle Scholar
  2. 2.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE International Conference of Evolutionary Computation, Anchorage, Alaska, pp. 69–73 (1998)Google Scholar
  3. 3.
    Shi, Y., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization. In: Proceedings of the Congress on Evolutionary Computation, Seoul, Korea (2001)Google Scholar
  4. 4.
    Angeline, P.J.: Using selection to improve particle swarm optimization. In: Proc. IEEE Int. Conf. on Evolutionary Computation, Anchorage, pp. 84–89 (1998)Google Scholar
  5. 5.
    Jing, L., Weicai, Z., Fang, L., Licheng, J.: Numerical optimal using organizational evolutionary algorithm. In: The Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications, September 2003, pp. 284–289. IEEE Computer Society Publisher, Xi’an, China (2003)Google Scholar
  6. 6.
    Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration. IEEE Transactions on Evolutionary Computation 8(3), 240–255 (2004)CrossRefGoogle Scholar
  7. 7.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evolutionary Computation. 3(2), 82–102 (1999)CrossRefGoogle Scholar
  8. 8.
    Wilcox, J.R.: Organizational learning with a learning Classifier System, IlliGAL Report NO.95003 (1995)Google Scholar
  9. 9.
    Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lin Cong
    • 1
  • Yuheng Sha
    • 1
  • Licheng Jiao
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

Personalised recommendations