Advertisement

An Heterogeneous Particle Swarm Optimizer with Predator and Scout Particles

  • Arlindo Silva
  • Ana Neves
  • Teresa Gonçalves
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7326)

Abstract

We present a new heterogeneous particle swarm optimization algorithm, called scouting predator-prey optimizer. This algorithm uses the swarm’s interactions with a predator particle to control the balance between exploration and exploitation. Scout particles are proposed as a straightforward way of introducing new exploratory behaviors into the swarm. These can range from new heuristics that globally improve the algorithm to modifications based on problem specific knowledge. The scouting predator-prey optimizer is compared with several variations of both particle swarm and differential evolution algorithms on a large set of benchmark functions, selected to present the algorithms with different difficulties. The experimental results suggest the new optimizer can outperform the other approaches over most of the benchmark problems.

Keywords

swarm intelligence particle swarm optimization heterogeneous particle swarms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    Beyer, H.-G., Schwefel, H.-P.: Evolution strategies - a comprehensive introduction. Natural Computing 1, 3–52 (2002)MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Engelbrecht, A.: Heterogeneous Particle Swarm Optimization. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 191–202. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Gao, H., Xu, W.: A new particle swarm algorithm and its globally convergent modifications. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(5), 1334–1351 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gao, H., Xu, W.: Particle swarm algorithm with hybrid mutation strategy. Applied Soft Computing 11(8), 5129–5142 (2011)CrossRefGoogle Scholar
  6. 6.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings. IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  7. 7.
    Montes de Oca, M., Pena, J., Stutzle, T., Pinciroli, C., Dorigo, M.: Heterogeneous particle swarm optimizers. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 698–705 (May 2009)Google Scholar
  8. 8.
    Omran, M.G.H., Engelbrecht, A.P.: Free search differential evolution. In: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, CEC 2009, pp. 110–117. IEEE Press, Piscataway (2009)CrossRefGoogle Scholar
  9. 9.
    Petalas, Y., Parsopoulos, K., Vrahatis, M.: Memetic particle swarm optimization. Annals of Operations Research 156, 99–127 (2007)MATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. App., 4:1–4:10 (January 2008)Google Scholar
  11. 11.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intelligence 1, 33–57 (2007)CrossRefGoogle Scholar
  12. 12.
    Rahnamayan, S., Tizhoosh, H., Salama, M.: Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation 12(1), 64–79 (2008)CrossRefGoogle Scholar
  13. 13.
    Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 3, p. 3 vol. (xxxvii+2348) (1999)Google Scholar
  14. 14.
    Silva, A., Neves, A., Costa, E.: An Empirical Comparison of Particle Swarm and Predator Prey Optimisation. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds.) AICS 2002. LNCS (LNAI), vol. 2464, pp. 103–110. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)MATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Tizhoosh, H.R.: Opposition-based learning: A new scheme for machine intelligence. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005, vol. 01, pp. 695–701. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  17. 17.
    Wang, H., Li, H., Liu, Y., Li, C., Zeng, S.: Opposition-based particle swarm algorithm with cauchy mutation. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 4750–4756 (September 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arlindo Silva
    • 1
  • Ana Neves
    • 1
  • Teresa Gonçalves
    • 2
  1. 1.Escola Superior de TecnologiaInstituto Politécnico de Castelo BrancoPortugal
  2. 2.Universidade de ÉvoraPortugal

Personalised recommendations