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Expand-and-Reduce Algorithm of Particle Swarm Optimization

  • Eiji Miyagawa
  • Toshimichi Saito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4984)

Abstract

This paper presents an optimization algorithm: particle swarm optimization with expand-and-reduce ability. When particles are trapped into a local optimal solution, a new particle is added and the trapped particle(s) can escape from the trap. The deletion of the particle is also used in order to suppress excessive network grows. The algorithm efficiency is verified through basic numerical experiments.

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References

  1. 1.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc of IEEE/ICNN, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Engelbrecht, A.P.: Computational Intelligence, an introduction, pp. 185–198. Wiley, Chichester (2004)Google Scholar
  3. 3.
    Richer, T.J., Blackwell, T.M.: The Levy Particle Swarm. In: Proc. Congr. Evol. Comput., pp. 3150–3157 (2006)Google Scholar
  4. 4.
    Parrott, D., Li, X.: Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput. 10(4), 440–458 (2006)CrossRefGoogle Scholar
  5. 5.
    Brits, R., Engelbrecht, A.P., van den Bergh, F.: A Niching Particle Swarm Optimizer. In: Proc. of SEAL, vol. 1079 (2002)Google Scholar
  6. 6.
    Hu, X., Eberhart, R.C.: Adaptive Particle Swarm Optimization: Detection and Response to Dynamic Systems. In: Proc. of IEEE/CEC, pp. 1666–1670 (2002)Google Scholar
  7. 7.
    Franken, N., Engelbrecht, A.P.: Particle swarm optimization approaches to coevolve strategies for the Iterated Prisoner’s Dilemma. IEEE Trans. Evol. Comput. 9(6), 562–579 (2005)CrossRefGoogle Scholar
  8. 8.
    Neethling, M., Engelbrecht, A.P.: Determining RNA secondary structure using set-based particle swarm optimization. In: Proc. Congr. Evol. Comput., pp. 6134–6141 (2006)Google Scholar
  9. 9.
    Jatmiko, W., Sekiyama, K., Fukuda, T.: A PSO-based mobile sensor network for odor source localization in dynamic environment: theory, simulation and measurement. In: Proc. Congr. Evol. Comput., pp. 3781–3788 (2006)Google Scholar
  10. 10.
    Tong, G., Fang, Z., Xu, X.: A particle swarm optimized particle filter for nonlinear system state estimation. In: Proc. Congr. Evol. Comput., pp. 1545–1549 (2006)Google Scholar
  11. 11.
    Oshime, T., Saito, T., Torikai, H.: ART-based parallel learning of growing SOMs and its application to TSP. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4232, pp. 1004–1011. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Bersini, H., Dorigo, M., Langerman, S., Geront, G., Gambardella, L.: Results of the first international contest on evolutionary optimisation (1st iceo). In: Proc. of IEEE/ICEC, pp. 611–615 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Eiji Miyagawa
    • 1
  • Toshimichi Saito
    • 1
  1. 1.Hosei University, KoganeiTokyoJapan

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