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Hybrid Particle Swarm Optimization: An Examination of the Influence of Iterative Improvement Algorithms on Performance

  • Jens Gimmler
  • Thomas Stützle
  • Thomas E. Exner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

In this article, we study hybrid Particle Swarm Optimization (PSO) algorithms for continuous optimization. The algorithms combine a PSO algorithm with either the Nelder-Mead-Simplex or Powell’s Direction-Set local search methods. Local search is applied each time the PSO part meets some convergence criterion. Our experimental results for test functions with up to 100 dimensions indicate that the usage of the iterative improvement algorithms can strongly improve PSO performance but also that the preferable choice of which local search algorithm to apply depends on the test function. The results also suggest that another main contribution of the local search is to make PSO algorithms more robust with respect to their parameter settings.

Keywords

Particle Swarm Optimiza Local Search Inertia Weight Local Search Algorithm Standard Particle Swarm Optimiza 
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.

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References

  1. 1.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), pp. 69–73 (1998)Google Scholar
  3. 3.
    Chatterjee, A., Siarry, P.: Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers and Operations Research 33(3), 859–871 (2006)MATHCrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation 8(3), 204–210 (2004)CrossRefGoogle Scholar
  6. 6.
    Hoos, H.H., Stützle, T.: Stochastic Local Search-Foundations and Applications. Morgan Kaufmann Publishers, San Francisco (2004)Google Scholar
  7. 7.
    Fan, S., Liang, Y., Zahara, E.: Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions. Engineering Optimization 36(4), 401–418 (2004)CrossRefGoogle Scholar
  8. 8.
    Wang, F., Qiu, Y., Bai, Y.: A new hybrid NM method and particle swarm algorithm for multimodal function optimization. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 497–508. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. (1992)Google Scholar
  10. 10.
    Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, 2000, vol. 1, pp. 84–88 (2000)Google Scholar
  11. 11.
    Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 3, pp. 1931–1938 (1999)Google Scholar
  12. 12.
    Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85(6), 317–325 (2003)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. Journal of Global Optimization 31(4), 635–672 (2005)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jens Gimmler
    • 1
  • Thomas Stützle
    • 2
  • Thomas E. Exner
    • 1
  1. 1.Theoretische Chemische DynamikUniversität KonstanzKonstanzGermany
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

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