A Taxonomy of Heterogeneity and Dynamics in Particle Swarm Optimisation

  • Harry Goldingay
  • Peter R. Lewis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)

Abstract

We propose a taxonomy for heterogeneity and dynamics of swarms in PSO, which separates the consideration of homogeneity and heterogeneity from the presence of adaptive and non-adaptive dynamics, both at the particle and swarm level. It supports research into the separate and combined contributions of each of these characteristics. An analysis of the literature shows that most recent work has focussed on only parts of the taxonomy. Our results agree with prior work that both heterogeneity, where particles exhibit different behaviour from each other at the same point in time, and dynamics, where individual particles change their behaviour over time, are useful. However while heterogeneity does typically improve PSO, this is often dominated by the improvement due to dynamics. Adaptive strategies used to generate heterogeneity may end up sacrificing the dynamics which provide the greatest performance increase. We evaluate exemplar strategies for each area of the taxonomy and conclude with recommendations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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
  2. 2.
    Montes de Oca, M.A., Peña, J., Stützle, T., Pinciroli, C., Dorigo, M.: Heterogeneous particle swarm optimizers. In: 2009 IEEE Congress on Evolutionary Computation, pp. 698–705. IEEE Press (2009)Google Scholar
  3. 3.
    Engelbrecht, A.P.: Heterogeneous particle swarm optimization. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 191–202. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Li, C., Yang, S., Nguyen, T.T.: A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(3), 627–646 (2012)CrossRefGoogle Scholar
  5. 5.
    Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous PSO inspired by ants. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 188–195. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Spanevello, P., Montes de Oca, M.A.: Experiments on adaptive heterogeneous PSO algorithms. Technical Report 2009-024, IRIDIA (2009)Google Scholar
  7. 7.
    Li, C., Yang, S.: An adaptive learning particle swarm optimizer for function optimization. In: 2009 IEEE Congress on Evolutionary Computation, pp. 381–388. IEEE Press (2009)Google Scholar
  8. 8.
    Nepomuceno, F., Engelbrecht, A.: A self-adaptive heterogeneous PSO for real-parameter optimization. In: 2013 IEEE Conference on Evolutionary Computation, pp. 361–368. IEEE Press (2013)Google Scholar
  9. 9.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE Congress on Evolutionary Computation, pp. 69–73. IEEE Press (1998)Google Scholar
  10. 10.
    Chatterjee, A., Siarry, P.: Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers & Operations Research 33(3), 859–871 (2006)CrossRefMATHGoogle Scholar
  11. 11.
    Shi, Y., Eberhart, R.: Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 101–106 (2001)Google Scholar
  12. 12.
    Zhan, Z.H., Zhang, J., Li, Y., Chung, H.H.: Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(6), 1362–1381 (2009)CrossRefGoogle Scholar
  13. 13.
    Neshat, M.: Faipso: Fuzzy adaptive informed particle swarm optimization. Neural Computing and Applications 23(1), 95–116 (2013)CrossRefGoogle Scholar
  14. 14.
    Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer – the ARPSO. Technical Report 2002-02, Aarhus UniversityGoogle Scholar
  15. 15.
    Evers, G., Ben Ghalia, M.: Regrouping particle swarm optimization: A new global optimization algorithm with improved performance consistency across benchmarks. In: IEEE International Conference on Systems, Man and Cybernetics 2009, pp. 3901–3908 (October 2009)Google Scholar
  16. 16.
    Clerc, M.: Standard Particle Swarm Optimisation. Technical Report hal-00764996, HAL (2012)Google Scholar
  17. 17.
    Kennedy, J.: Bare bones particle swarms. In: 2003 IEEE Swarm Intelligence Symposium, pp. 80–87. IEEE Press (2003)Google Scholar
  18. 18.
    Blackwell, T.M., Bentley, P.J.: Dynamic search with charged swarms. In: Genetic and Evolutionary Computation Conference, GECCO 2002, pp. 19–26. Morgan Kaufmann, San Fransisco (2002)Google Scholar
  19. 19.
    Baskar, S., Suganthan, P.N.: A novel concurrent particle swarm optimization. In: 2004 IEEE Congress on Evolutionary Computation, pp. 792–796. IEEE Press (2004)Google Scholar
  20. 20.
    Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  21. 21.
    Liu, Y., Qin, Z., Shi, Z., Lu, J.: Center particle swarm optimization. Neurocomputing 70(4-6), 672–679 (2007)CrossRefGoogle Scholar
  22. 22.
    Pongchairerks, P., Kachitvichyanukul, V.: Non-homogenous particle swarm optimization with multiple social structures. In: Proceedings of the 2005 International Conference on Simulation and Modeling, pp. 137–144. Asian Institute of Technology, Bangkok (2005)Google Scholar
  23. 23.
    Di Chio, C., Di Chio, P., Giacobini, M.: An evolutionary game-theoretical approach to particle swarm optimisation. In: Giacobini, M., et al. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 575–584. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Finck, S., Hansen, N., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2010: Presentation of the noisy functions. Technical Report RR-7215, INRIA (2010)Google Scholar
  25. 25.
    Goldingay, H., Lewis, P.R.: Experimental results concerning heterogeneity and dynamics in particle swarm optimisation. Technical Report AISA-14-01, Aston Institute for Systems Analytics, Aston University, UK (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Harry Goldingay
    • 1
  • Peter R. Lewis
    • 1
  1. 1.Aston Lab for Intelligent Collectives Engineering (ALICE), Aston Institute for Systems AnalyticsAston UniversityBirminghamUK

Personalised recommendations