Advertisement

High Dimension Complex Functions Optimization Using Adaptive Particle Swarm Optimizer

  • Kaiyou Lei
  • Yuhui Qiu
  • Xuefei Wang
  • He Yi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4062)

Abstract

Due to the existence of large numbers of local and global optima of high dimension complex functions, general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima. In this paper, an adaptive particle swarm optimizer with a better search performance is proposed, which employ a novel dynamic inertia weight curves and mutate global optimum to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the proposed algorithm and compare it with other published methods on several high dimension complex functions, the experimental results demonstrate that this revised algorithm can rapidly converge at high quality solutions.

Keywords

Particle swarm optimizer convergence premature problem 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press Center, Piscataway, NJ (1995)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 1, 58–73 (2002)CrossRefGoogle Scholar
  3. 3.
    Hu, X., Eberhart, R.C., Shi, Y.H.: Engineering optimization with particle swarm. In: Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA, pp. 53–57 (2003)Google Scholar
  4. 4.
    Shi, Y.H., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1945–1950. IEEE Press Center, Piscataway, NJ (1999)Google Scholar
  5. 5.
    Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 69–73. IEEE Press Center, Piscataway, NJ (1998)Google Scholar
  6. 6.
    Angeline, P.: Using selection to improve particle swarm optimization. In: Proceedings of IJCNN 1999, Washington, USA, pp. 84–89 (1999)Google Scholar
  7. 7.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particles swarm theory. In: Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Service Center, Piscataway (1995)CrossRefGoogle Scholar
  8. 8.
    Lei, K.Y., Qiu, Y.H., He, Y.: A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization. In: 1st International Symposium on Systems and Control in Aerospace and Astronautics, Harbin, China, pp. 342–346 (2006)Google Scholar
  9. 9.
    Lv, Z.S., Hou, Z.R.: Particle swarm optimization with adaptive mutation. Acta Electrronica Sinica 3, 416–420 (2004)Google Scholar
  10. 10.
    Zeng, J.C., Cui, Z.H.: A guaranteed global convergence particle swarm optimizer. Journal of computer research and development 8, 1334–1338 (2004)Google Scholar
  11. 11.
    Li, B.Y., Xiao, Y.S., Wang, L.: A hybrid particle swarm optimization algorithm for solving complex functions with high dimensions. Information and Control 1, 30–37 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kaiyou Lei
    • 1
  • Yuhui Qiu
    • 1
  • Xuefei Wang
    • 1
  • He Yi
    • 1
  1. 1.Faculty of Computer & Information ScienceSouthwest UniversityChongqingP.R. China

Personalised recommendations