Advertisement

Crossed Particle Swarm Optimization Algorithm

  • Teng-Bo Chen
  • Yin-Li Dong
  • Yong-Chang Jiao
  • Fu-Shun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

Keywords

Swarm Intelligence Benchmark Function Integral Control Contractive Factor Base 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Clerc, M., Kennedy, J.: The Particle Swarm—Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)CrossRefGoogle Scholar
  2. 2.
    Parsopoulos, K.E., Vrahatis, M.N.: On the Computation of All Global Minimizers Through Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8, 211–224 (2004)CrossRefGoogle Scholar
  3. 3.
    Shi, Y.H., Eberhat, R.C.: A Modified Particle Swarm Optimization. In: Proceedings of IEEE International Congress on Evolutionary Computation, pp. 69–73 (1998)Google Scholar
  4. 4.
    Thanmaya, P., Kalyan, V., Chilukuri, K.M.: Fitness-Distance-Ratio Based Particle Swarm Optimization. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 174–181 (2003)Google Scholar
  5. 5.
    Kennedy, J.: Small Worlds and Megaminds: Effects of Neighbourhood Topology on Particle Swarm Performance. In: Proceedings of the 1999 Congress Evolutionary Computation, vol. 3, pp. 1931–1938. IEEE Press, Los Alamitos (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Teng-Bo Chen
    • 1
  • Yin-Li Dong
    • 1
  • Yong-Chang Jiao
    • 1
  • Fu-Shun Zhang
    • 1
  1. 1.National Laboratory of Antennas and Microwave TechnologyXidian UniversityXi’anP.R. China

Personalised recommendations