Advertisement

Opposition-Based Learning Fully Informed Particle Swarm Optimizer without Velocity

  • Ying Gao
  • Lingxi Peng
  • Fufang Li
  • Miao Liu
  • Waixi Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

By applying full information and employing the notion of opposition-based learning, a new opposition based learning fully information particle swarm optimiser without velocity is proposed for optimization problems. Different from the standard PSO, particles in swarm only have position without velocity and the personal best position gets updated using opposition-based learning in the algorithm. Besides, all personal best positions are considered to update particle position. The theoretical analysis for the proposed algorithm implies that the particle of the swarm tends to converge to a weighted average of all personal best position. Because of discarding the particle velocity, and using full information and opposition-based learning, the algorithm is the simpler and more effective. The proposed algorithm is applied to some well-known benchmarks. The relative experimental results show that the algorithm achieves better solutions and faster convergence.

Keywords

particle swarm optimizer opposition-based learning full information 

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 the IEEE Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Perth (1995)Google Scholar
  2. 2.
    Tizhoosh, H.R.: Opposition-Based Learning: A New Scheme for Machine Intelligence. In: Int. Conf. on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), Vienna, Austria, vol. I, pp. 695–701 (2005)Google Scholar
  3. 3.
    Jabeen, H., Jalil, Z., Baig, A.R.: Opposition based initialization in particle swarm optimization O-PSO. In: GECCO Companion, pp. 2047–2052. ACM (2009)Google Scholar
  4. 4.
    Wang, H., Liu, Y., Li, C., Zeng, S.: Opposition-based Particle Swarm Algorithm with Cauchy Mutation. In: IEEE CEC, pp. 4750–4756 (2007)Google Scholar
  5. 5.
    Shahzad, F., Baig, A.R., Masood, S., Kamran, M., Naveed, N.: Opposition-Based Particle Swarm Optimization with Velocity Clamping (OVCPSO). Advances in Intelligent and Soft Computing 116, 339–348 (2009)CrossRefGoogle Scholar
  6. 6.
    van den Bergh, F.: An analysis of particle swarm optimizers. PhD thesis, Department of Computer Science, University of Pretoria, South Africa (2002)Google Scholar
  7. 7.
    van den Bergh, F., Engelbrecht, A.: A study of particle swarm optimization particle trajectories. Information Sciences 176(8), 937–971 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    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 (2002)CrossRefGoogle Scholar
  9. 9.
    Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85, 317–325 (2003)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ying Gao
    • 1
  • Lingxi Peng
    • 1
  • Fufang Li
    • 1
  • Miao Liu
    • 1
  • Waixi Liu
    • 1
  1. 1.Department of Computer Science and Technology, Guangzhou Higher Education Mega CenterGuangzhou UniversityGuangzhouP.R. China

Personalised recommendations