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

  • Guoliang Huang
  • Xinling Shi
  • Zhenzhou An
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


To study how the different number of particles in clustering affect the performance of two-layer particle swarm optimization (TLPSO) that set the global best location in each swarm of the bottom layer to be the position of the particle in the swarm of the top layer, fourteen 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 of the bottom layer when the function dimension is increasing from low to high.


Particle swarm optimization hierarchy cluster 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  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., Liu, C.M., Huang, C.C., Wu, X.N.: Improved particle swarm algorithm for hydrological parameter optimization. App. Math. Comp. 217, 3207–3215 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Jiang, Y., Hu, T., Huang, C.C., Wu, X.: An improved particle swarm optimization algorithm. App. Math. Comp. 193, 231–239 (2007)zbMATHCrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Chen, C.C.: Two-layer particle swarm optimization for unconstrained optimization problems. App. Soft. Comp. 11, 295–304 (2011)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
  1. 1.School of Information Science and Engineering Yunnan UniversityKunmingChina

Personalised recommendations