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The Performance Measurement of a Canonical Particle Swarm Optimizer with Diversive Curiosity

  • Hong Zhang
  • Jie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

For improving the search performance of a canonical particle swarm optimizer (CPSO), we propose a newly canonical particle swarm optimizer with diversive curiosity (CPSO/DC). A crucial idea here is to introduce diversive curiosity into the CPSO to comprehensively manage the trade-off between exploitation and exploration for alleviating stagnation. To demonstrate the effectiveness of the proposed method, computer experiments on a suite of five-dimensional benchmark problems are carried out. We investigate the characteristics of the CPSO/DC, and compare the search performance with other methods. The obtained results indicate that the search performance of the CPSO/DC is superior to that by EPSO, ECPSO and RGA/E, but is inferior to that by PSO/DC for the Griewank and Rastrigin problems.

Keywords

canonical particle swarm optimizer evolutionary particle swarm optimization real-coded genetic algorithm exploitation and exploration model selection specific and diversive curiosity 

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References

  1. 1.
    Berlyne, D.: Conflict, Arousal, and Curiosity. McGraw-Hill Book Co., New York (1960)CrossRefGoogle Scholar
  2. 2.
    Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2000)CrossRefGoogle Scholar
  3. 3.
    Day, H.: Curiosity and the Interested Explorer. Performance and Instruction 21(4), 19–22 (1982)CrossRefGoogle Scholar
  4. 4.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  5. 5.
    El-Abd, M., Kamel, M.S.: A Taxonomy of Cooperative Particle Swarm Optimizers. International Journal of Computational Intelligence Research 4(2), 137–144 (2008)CrossRefGoogle Scholar
  6. 6.
    Juang, C.-F.: A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design. IEEE Transactions on Systems, Man and Cybernetics Part B 34(2), 997–1006 (2004)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Piscataway, New Jersey, USA, pp. 1942–1948 (1995)Google Scholar
  8. 8.
    Kennedy, J.: In Search of the Essential Particle Swarm. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 6158–6165 (2006)Google Scholar
  9. 9.
    Lane, J., Engelbrecht, A., Gain, J.: Particle Swarm Optimization with Spatially Meaningful Neighbours. In: Proceedings of Swarm Intelligence Symposium (SIS 2008), St. Louis, MO, USA, pp. 1–8 (2008)Google Scholar
  10. 10.
    Loewenstein, G.: The Psychology of Curiosity: A Review and Reinterpretation. Psychological Bulletin 116(1), 75–98 (1994)CrossRefGoogle Scholar
  11. 11.
    Poli, R.: Analysis of the Publications on the Applications of Particle Swarm Optimisation. Journal of Artificial Evolution and Applications 2008(1), 1–10 (2008)Google Scholar
  12. 12.
    Reyes-Sierra, M., Coello, C.A.C.: Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)MathSciNetGoogle Scholar
  13. 13.
    Wohlwill, J.F.: A Conceptual Analysis of Exploratory Behavior in Advances in Intrinsic Motivation and Aesthetics. Plenum Press, New York (1981)Google Scholar
  14. 14.
    Zhang, H., Ishikawa, M.: Characterization of particle swarm optimization with diversive curiosity. Journal of Neural Computing & Applications, 409–415 (2009)Google Scholar
  15. 15.
    Zhang, H., Ishikawa, M.: Particle Swarm Optimization with Diversive Curiosity and Its Identification. In: Ao, S., et al. (eds.) Trends in Communication Technologies and Engineering Science. Lecture Notes in Electrical Engineering, vol. 33, pp. 335–349. Springer, Netherlands (2009)CrossRefGoogle Scholar
  16. 16.
    Zhang, H., Ishikawa, M.: The performance verification of an evolutionary canonical particle swarm optimizer. Neural Networks 23(4), 510–516 (2010)CrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hong Zhang
    • 1
  • Jie Zhang
    • 2
  1. 1.Department of Brain Science and EngineeringKyushu Institute of TechnologyKitakyushuJapan
  2. 2.Wuxi Bowen Software Technology Co., LtdWuxiChina

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