Advertisement

The Comparative Study of Different Number of Particles in Clustering Based on Three-Layer Particle Swarm Optimization

  • Guoliang Huang
  • Xinling Shi
  • Zhenzhou An
  • He Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

To study how the different number of particles in clustering affect the performance of three-layer particle swarm optimization (THLPSO) that sets the global best location in each swarm to be the position of the particle in the swarm of the next layer, ten configurations of the different number of particles are compared. Fourteen benchmark functions, being in seven types with different circumstance, are used in the experiments. The experiments show that the searching ability of the algorithms is related to the number of particles in clustering, which is better with the number of particles transforming from as little as possible to as much as possible in each swarm when the function dimension is increasing from less to more. Finally, the original algorithm and THLPSO are compared to illustrate the efficiency of the proposed method.

Keywords

Particle swarm optimization hierarchy cluster 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Janson, S., Middendorf, M.: A Hierarchical Particle Swarm Optimizer and Its Adaptive Variant. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 35, 1272–1282 (2005)CrossRefGoogle Scholar
  2. 2.
    Kennedy, J.: Stereotyping: Improving Particle Swarm Performance with Cluster Analysis. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1507–1512 (2000)Google Scholar
  3. 3.
    Jiang, Y., Hu, T., Huang, C.C., Wu, X.: An Improved Particle Swarm Optimization algorithm. App. Math. Comp. 193, 231–239 (2007)zbMATHCrossRefGoogle Scholar
  4. 4.
    Chen, D.B., Zhao, C.X.: Particle Swarm Optimization with Adaptive Population Size and Its Application. App. Soft. Comp. 9, 39–48 (2009)CrossRefGoogle Scholar
  5. 5.
    Chen, C.C.: Two-layer Particle Swarm Optimization for Unconstrained Optimization Problems. App. Soft. Comp. 11, 295–304 (2011)CrossRefGoogle Scholar
  6. 6.
    Huang, G., Shi, X., An, Z.: The Comparative Study of Different Number of Particles in Clustering Based on Two-Layer Particle Swarm Optimization. In: Tan, Y., Shi, Y., Ji, Z. (eds.) ICSI 2012, Part I. LNCS, vol. 7331, pp. 109–115. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Bratton, D., Kennedy, J.: Defining a Standard for Particle Swarm Optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium, pp. 120–127. IEEE Press, Honolulu (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guoliang Huang
    • 1
  • Xinling Shi
    • 1
  • Zhenzhou An
    • 1
  • He Sun
    • 1
  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingChina

Personalised recommendations