Advertisement

From Theory to Practice in Particle Swarm Optimization

  • Maurice Clerc
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 8)

Summary

The purpose of this chapter is to draw attention to two points that are not always well understood, namely, a) the “balance” between exploitation and exploration may be not what we intuitively think, and b) a mean best result may be meaningless. The second point is obviously quite important when two algorithms are compared. These are discussed in the appendix. We believe that these points would be useful to researchers in the field for analysis and comparison of algorithms in a better and rigorous way, and help them design new powerful tools.

Keywords

Particle Swarm Optimization Search Space Particle Swarm Exploitation Rate Solution Point 
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.
    Ben Ghalia, M.: Particle swarm optimization with an improved exploration-exploitation balance. In: Circuits and Systems, MWSCAS, pp. 759–762. Circuits and Systems, MWSCAS (2008)Google Scholar
  2. 2.
    CEC. Congress on Evolutionary Computation Benchmarks (2005), http://www3.ntu.edu.sg/home/epnsugan/
  3. 3.
    Clerc, M.: Math Stuff about PSO, http://clerc.maurice.free.fr/pso/
  4. 4.
    Clerc, M.: Particle Swarm Optimization. ISTE (International Scientific and Technical Encyclopedia) (2006)Google Scholar
  5. 5.
    Clerc, M.: Stagnation analysis in particle swarm optimization or what happens when nothing happens, Technical report (2006), http://hal.archives-ouvertes.fr/hal-00122031
  6. 6.
    Clerc, M.: Why does it work? International Journal of Computational Intelligence Research 4(2), 79–91 (2008)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Fernandez-Martinez, J.L., Garcia-Gonzalo, E., Fernandez-Alvarez, J.P.: Theoretical analysis of particle swarm trajectories through a mechanical analogy. International Journal of Computational Intelligent Research (this issue, 2007)Google Scholar
  9. 9.
    Gacôgne, L.: Steady state evolutionary algorithm with an operator family. In: EISCI, Kosice, Slovaquie, pp. 373–379 (2002)Google Scholar
  10. 10.
    Helwig, S., Wanka, R.: Particle swarm optimization in high-dimensional bounded search spaces. In: IEEE Swarm Intelligence Symposium (SIS 2007) (2007)Google Scholar
  11. 11.
    Helwig, S., Wanka, R.: Theoretical analysis of initial particle swarm behavior. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 889–898. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Kennedy, J.: Bare Bones Particle Swarms. In: IEEE Swarm Intelligence Symposium, pp. 80–87 (2003) (Fully informed PSO)Google Scholar
  13. 13.
    Kennedy, J.: How it works: Collaborative Trial and Error. International Journal of Computational Intelligence Research 4(2), 71–78 (2008)CrossRefGoogle Scholar
  14. 14.
    Koduru, P., et al.: A particle swarm optimization-nelder mead hybrid algorithm for balanced exploration and exploitation in multidimensional search space (2006)Google Scholar
  15. 15.
    Langdon, W., Poli, R.: Evolving problems to learn about particle swarm and other optimisers. In: Congress on Evolutionary Computation, pp. 81–88 (2005)Google Scholar
  16. 16.
    Li, N., Sun, D., Zou, T., Qin, Y., Wei, Y.: Analysis for a particle’s trajectory of pso based on difference equation. Jisuanji Xuebao/Chinese Journal of Computers 29(11), 2052–2061 (2006)Google Scholar
  17. 17.
    Marsaglia, G., Zaman, A.: The kiss generator. Technical report, Dept. of Statistics, U. of Florida (1993)Google Scholar
  18. 18.
    Mendes, R.: Population Topologies and Their Influence in Particle Swarm Performance. PhD thesis, Universidade do Minho (2004)Google Scholar
  19. 19.
    Onwubolu, G.C., Babu, B.V.: New Optimization Techniques in Engineering. Springer, Berlin (2004)zbMATHGoogle Scholar
  20. 20.
    Parsopoulos, K.E., Vrahatis, M.N.: Parameter selection and adaptation in unified particle swarm optimization. Mathematical and Computer Modelling 46, 198–213 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Poli, R.: The Sampling Distribution of Particle Swarm Optimisers and their Stability. Technical report, University of Essex (2007); Poli, R.: On the moments of the sampling distribution of particle swarm optimisers. GECCO (Companion), 2907–2914 (2007)Google Scholar
  22. 22.
    Poli, R.: Dynamics and stability of the sampling distribution of particle swarm optimisers via moment analysis. Journal of Artificial Evolution and Applications (2008)Google Scholar
  23. 23.
    Poli, R., Langdon, W.B., Clerc, M., Stephen, C.R.: Continuous Optimisation Theory Made Easy? Finite-Element Models of Evolutionary Strategies, Genetic Algorithms and Particle Swarm Optimizers. In: Stephens, C.R., et al. (eds.) Foundations of Genetic Algorithms, Mexico, vol. 9, pp. 165–193. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    PSC. Particle Swarm Central, http://www.particleswarm.info
  25. 25.
    Richards, M., Ventura, D.: Dynamic Sociometry and Population Size in Particle Swarm Optimization. C’est juste un extrait de la thèse (2003)Google Scholar
  26. 26.
    Sandgren, E.: Non linear integer and discrete programming in mechanical design optimization (1990) ISSN 0305-2154Google Scholar
  27. 27.
    Shang, Y.-W., Qiu, Y.-H.: A note on the extended rosenbrock function. Evolutionary Computation 14(1), 119–126 (2006)CrossRefGoogle Scholar
  28. 28.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)CrossRefGoogle Scholar
  29. 29.
    Wong, T.-T., Luk, W.-S., Heng, P.-A.: Sampling with Hammersley and Halton points. Journal of Graphics Tools 2(2), 9–24 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maurice Clerc

There are no affiliations available

Personalised recommendations