Advertisement

Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning

  • J. J. Liang
  • P. N. Suganthan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

This paper proposes an Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning (AH-CLPSO) based on the previous proposed Learning Particle Swarm Optimizer (CLPSO) [1], which is good at multimodal problems but converges slow on single modal problems. A self-adaptation technique is introduced to adjust the learning probability adaptively in the search process and the historical information is used in the velocity update equation to search more effectively. The experiment results show that the history learning strategy and the adaptation technique improves the performance of CLPSO on problems which need fast convergence and achieve comparable results on the problems requiring slow convergence.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liang, J.J., Suganthan, P.N., Qin, A.K., Baskar, S.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Transactions on Evolutionary Computation, June 2006 (to appear)Google Scholar
  2. 2.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Int. Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. IEEE Congress on Evolutionary Computation, pp. 69–73 (1998)Google Scholar
  5. 5.
    Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  6. 6.
    Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: P. IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii USA (2002)Google Scholar
  7. 7.
    Parsopoulos, K.E., Vrahatis, M.N.: UPSO- A Unified Particle Swarm Optimization Scheme. Lecture Series on Computational Sciences, pp. 868–873 (2004)Google Scholar
  8. 8.
    Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Transactions on Evolutionary Computation 8(3), 204–210 (2004)CrossRefGoogle Scholar
  9. 9.
    Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: P. Swarm Intelligence Symposium, Indiana, USA, pp. 174–181 (2003)Google Scholar
  10. 10.
    van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3), 225–239 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. J. Liang
    • 1
  • P. N. Suganthan
    • 1
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

Personalised recommendations