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A Set-Based Particle Swarm Optimization Method

  • Christian B. Veenhuis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

The representation used in Particle Swarm Optimization (PSO) is an n-dimensional vector. If you want to apply the PSO method, you have to encode your problem as fix-sized vector. But many problem domains have solutions of unknown sizes as for instance in data clustering where you often don’t know the number of clusters in advance.

In this paper a set-based PSO is proposed which replaces the position and velocity vectors by position and velocity sets realizing this way a PSO with variable length representation. All operations of the PSO update equations are redefined in an appropriate manner. Additionally, an operator reducing set bloating effects is introduced.

The presented approach is applied to well-known data clustering problems and performs better as other algorithms on them.

Keywords

Particle Swarm Optimization Reduction Operator Dynamic Bayesian Network Particle Swarm Optimization Method Standard Particle Swarm Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, Chichester (2005)Google Scholar
  2. 2.
    Handl, J., Knowles, J., Dorigo, M.: On the performance of ant-based clustering. In: Proc. 3rd International Conference on Hybrid Intelligent Systems, pp. 204–213. IOS Press, AmsterdamGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Perth, Australia. IEEE Service Center, Piscataway (1995)Google Scholar
  4. 4.
    Kennedy, J.: Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 Conference on Evolutionary Computation, vol. 3, pp. 1931–1938 (1999)Google Scholar
  5. 5.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  6. 6.
    Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1671–1676 (2002)Google Scholar
  7. 7.
    Ku, S., Lee, B.: A Set-Oriented Genetic Algorithm and the Knapsack Problem. In: Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea, May 27-30 (2001)Google Scholar
  8. 8.
    Murphy, P.M., Aha, D.W.: UCI Repository of machine learning databases, Irvine, CA: University of California, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLRepository.html
  9. 9.
    Neethling, M., Engelbrecht, A.P.: Determining RNA Secondary Structure using Set-based Particle Swarm Optimization. In: Proceedings of the 2006 Congress on Evolutionary Computation, Vancouver, BC, July 16-21, pp. 1670–1677 (2006)Google Scholar
  10. 10.
    Raidl, G.R., Julstrom, B.A.: Edge-sets: An effective evolutionary coding of spanning trees. IEEE Transactions on Evolutionary Computation 7(3), 225–239 (2003)CrossRefGoogle Scholar
  11. 11.
    Shi, Y.H., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska (1998)Google Scholar
  12. 12.
    van der Merwe, D.W., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Canberra, Australia, vol. 1, pp. 215–220 (2003)Google Scholar
  13. 13.
    Veenhuis, C., Köppen, M.: Data Swarm Clustering. In: Abraham, A., Grosan, C., Ramos, V. (eds.) Swarm Intelligence and Data Mining, Studies in Computational Intelligence. Springer, Germany (2006)Google Scholar
  14. 14.
    Xing-Chen, H., Zheng, Q., Lei, T., Li-Ping, S.: Research on Structure Learning of Dynamic Bayesian Networks by Particle Swarm Optimization. In: Proceedings of the First IEEE Symposium on Artificial Life, Honolulu, Hawaii, April 1-5 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christian B. Veenhuis
    • 1
  1. 1.Fraunhofer IPK, Dept. Security TechnologyBerlinGermany

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