Advertisement

Local and Global Search Based PSO Algorithm

  • Yanxia Sun
  • Zenghui Wang
  • Barend Jacobus van Wyk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.

Keywords

Local search global search particle swarm optimisation 

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 Optimisation. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Hu, X., Shi, Y., Eberhart, R.: Recent Advances in Particle Swarm. In: Congress on Evolutionary Computation, pp. 90–97. IEEE Service Center, Piscataway (2004)Google Scholar
  3. 3.
    Huang, C.M., Huang, C.J., Wang, M.L.: A Particle Swarm Optimisation to Identifying the ARMAX Model for Short term Load Forecasting. IEEE Transactions on Power Systems 20, 1126–1133 (2005)CrossRefGoogle Scholar
  4. 4.
    Clerc, M.: Particle Swarm Optimisation. ISTE Publishing Company (2006)Google Scholar
  5. 5.
    Nedjah, N., Mourelle, L.D.M.: Systems Engineering Using Particle Swarm Optimisation. Nova Science Publishers (2007)Google Scholar
  6. 6.
    Aihara, K., Takabe, T., Toyoda, M.: Chaotic Neural Networks. Physics Letter A 144(6-7), 333–340 (1990)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimiser. In: IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)Google Scholar
  8. 8.
    Zhang, W.J., Xie, X.F.: DEPSO: Hybrid Particle Swarm with Differential Evolution Operator. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Washington DC, USA, pp. 3816–3821 (2003)Google Scholar
  9. 9.
    Mohagheghi, S., Del Valle, Y., Venayagamoorthy, G., Harley, R.: A Comparison of PSO and Back Propagation for Training RBF Neural Networks for Identification of a Power System with STATCO. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 381–384 (June 2005)Google Scholar
  10. 10.
    del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J., Harley, R.G.: Particle Swarm Optimisation: Basic Concepts, Variants and Applications in Power Systems. IEEE Transactions On Evolutionary Computation 12(2), 171–195 (2008)CrossRefGoogle Scholar
  11. 11.
    Doctor, S., Venayagamoorthy, G., Gudise, V.: Optimal PSO for Collective Robotic sSearch Applications. In: Proceeding IEEE Congress on Evolutionary Computation, Portland, Oregon, USA, pp. 1390–1395 (2004)Google Scholar
  12. 12.
    Venayagamoorthy, G.: Adaptive Critics for Dynamic Particle Swarm Optimisation. In: Proceedings of IEEE International Symposium on Intelligence Control, Taipei, Taiwan, pp. 380–384 (September 2004)Google Scholar
  13. 13.
    Liu, B., Wang, L., Jin, Y.H., Tang, F., Huang, D.X.: Improved Particle Swarm Optimisation Combined with chaos. Chaos, Solitons and Fractals 25, 1261–1271 (2005)zbMATHCrossRefGoogle Scholar
  14. 14.
    Kennedy, J., Clerc, M., et al.: Particle Swarm Central (2012), http://www.particleswarm.info/Programs.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yanxia Sun
    • 1
  • Zenghui Wang
    • 2
  • Barend Jacobus van Wyk
    • 1
  1. 1.Department of Electrical EngineeringTshwane University of TechnologyPretoriaSouth Africa
  2. 2.Department of Electrical and Mining EngineeringUniversity of South AfricaFloridaSouth Africa

Personalised recommendations