Advertisement

Dynamic Data Clustering Using Stochastic Approximation Driven Multi-Dimensional Particle Swarm Optimization

  • Serkan Kiranyaz
  • Turker Ince
  • Moncef Gabbouj
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

With an ever-growing attention Particle Swarm Optimization (PSO) has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. For this purpose we use simultaneous perturbation stochastic approximation (SPSA), which is applied only to the gbest (not to the entire swarm) for a low-cost solution. Since the problem of poor gbest update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), two distinct SA approaches are then integrated into MD-PSO and tested over a set of unsupervised data clustering applications. Experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.

Keywords

Particle Swarm Optimization stochastic approximation multi-dimensional search gradient descent dynamic data clustering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, A., Das, S., Roy, S.: Swarm Intelligence Algorithms for Data Clustering. In: Soft Computing for Knowledge Discovery and Data Mining book, Part IV, October 25, pp. 279–313 (2007)Google Scholar
  2. 2.
    Bäck, T., Schwefel, H.P.: An overview of evolutionary algorithm for parameter optimization. Evolutionary Computation 1, 1–23 (1993)CrossRefGoogle Scholar
  3. 3.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Cluster Validation Techniques. Journal of Intelligent Information Systems 17(2, 3), 107–145 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)Google Scholar
  5. 5.
    Kiranyaz, S., Ince, T., Yildirim, A., Gabbouj, M.: Fractional Particle Swarm Optimization in Multi-Dimensional Search Space. IEEE Trans. on Systems, Man, and Cybernetics (2009) (in print)Google Scholar
  6. 6.
    Maryak, J.L., Chin, D.C.: Global random optimization by simultaneous perturbation stochastic approximation. In: Proc. of the 33rd Conf. on Winter Simulation, Washington, DC, December 9-12, pp. 307–312 (2001)Google Scholar
  7. 7.
    Riget, J., Vesterstrom, J.S.: A Diversity-Guided Particle Swarm Optimizer - The ARPSO, Technical report, Department of Computer Science, University of Aarhus (2002)Google Scholar
  8. 8.
    Spall, J.C.: Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation. IEEE Transactions on Automatic Control 37, 332–341 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Spall, J.C.: Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans. on Aerospace and Electronic Systems 34, 817–823 (1998)CrossRefGoogle Scholar
  10. 10.
    Van den Bergh, F.: An Analysis of Particle Swarm Optimizers, PhD thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa (2002)Google Scholar
  11. 11.
    Yan, Y., Osadciw, L.A.: Density estimation using a new dimension adaptive particle swarm optimization algorithm. Journal of Swarm Intelligence 3(4) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Serkan Kiranyaz
    • 1
  • Turker Ince
    • 2
  • Moncef Gabbouj
    • 1
  1. 1.Tampere University of TechnologyTampereFinland
  2. 2.Izmir University of EconomicsIzmirTurkey

Personalised recommendations