Advertisement

Grouping-Shuffling Particle Swarm Optimization: An Improved PSO for Continuous Optimization

  • Yinghai Li
  • Xiaohua Dong
  • Ji Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evolutionary process. By testing on 4 benchmark functions, the numerical results demonstrate that, the optimization performance of the proposed GSPSO is much better than PSO and SFLA. The GSPSO can both avoid the PSO’s shortage that easy to get rid of the local optimal solution and has faster convergence speed and higher convergence precision than the PSO and SFLA.

Keywords

Particle swarm optimization Shuffled frog leaping algorithm Evolution strategy Continuous optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shelokar, P.S., Siarry, P., Jayaraman, V.K., Kulkarni, B.D.: Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied Mathematics and Computation 188(1), 129–142 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference Neural Networks, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Ebehtart, R.C., Kennedy, J.: A new optimizer using Particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Seienee, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  4. 4.
    Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management 129(3), 210–225 (2003)CrossRefGoogle Scholar
  5. 5.
    Elbeltagi, E., Hegazy, T., Grierson, D.: Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics 19(1), 43–53 (2005)CrossRefGoogle Scholar
  6. 6.
    Alireza, R.V., Ali, H.M.: Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm. Soft Comput. 12, 435–452 (2008)zbMATHCrossRefGoogle Scholar
  7. 7.
    Elbehairy, H., Elbeltagi, E., Hegazy, T., Soudki, K.: Comparison of Two Evolutionary Algorithms for Optimization of Bridge Deck Repairs. Computer-Aided Civil and Infrastructure Engineering 21, 561–572 (2006)CrossRefGoogle Scholar
  8. 8.
    Li, Y., Zhou, J., Yang, J., Liu, L., Qin, H., Yang, L.: The Chaos-based Shuffled Frog Leaping Algorithm and Its Application. In: Fourth International Conference on Natural Computation, vol. 1, pp. 481–485 (2008)Google Scholar
  9. 9.
    Eusuff, M.M., Lansey, K.E., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering Optimization 38(2), 129–154 (2006)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Shi, Y., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yinghai Li
    • 1
  • Xiaohua Dong
    • 1
  • Ji Liu
    • 1
  1. 1.College of Hydraulic & Environmental EngineeringChina Three Gorges UniversityYichangChina

Personalised recommendations