Avoidance Strategies in Particle Swarm Optimisation

  • Karl Mason
  • Enda HowleyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 378)


Particle swarm optimisation (PSO) is an optimisation algorithm in which particles traverse a problem space moving towards promising locations which either they or their neighbours have previously visited. This paper presents a new PSO variant with the Avoidance of Worst Locations (AWL). This variation was inspired by animal behaviour. In the wild, an animal will react to negative stimuli as well as positive, e.g. an animal looking for food will also be conscious of danger. PSO AWL enables particles to remember previous poor solutions as well as good. As a result, the particles change the way they move and avoid known bad areas. Balancing the influence of these poor locations is vital. The research in this paper found that a small influence from bad locations on the particles leads to a significant improvement on overall performance when compared to the standard PSO. When compared to previous implementations of worst location memory, PSO AWL demonstrates vast improvements.


  1. 1.
    Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Swarm Intelligence Symposium, SIS 2007, pp. 120–127. IEEE (2007)Google Scholar
  2. 2.
    Broderick, I., Howley, E.: Particle swarm optimisation with enhanced memory particles. In: Dorigo, M., Birattari, M., Garnier, S., Hamann, H., Montes de Oca, M., Solnon, C., Stützle, T. (eds.) ANTS 2014. LNCS, vol. 8667, pp. 254–261. Springer, Heidelberg (2014)Google Scholar
  3. 3.
    Clerc, M., Kennedy, J.: The particle swarm–explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    X. Hu, Eberhart, R.C., Shi, Y.: Particle swarm with extended memory for multiobjective optimization. In: Swarm Intelligence Symposium, SIS’03. Proceedings of the 2003 IEEE, pp. 193–197. IEEE (2003)Google Scholar
  6. 6.
    Jayabarathi, T., Kolipakula, R.T., Krishna, M.V., Yazdani, A.: Application and comparison of PSO, its variants and hde techniques to emission/economic dispatch. Arab. J. Sci. Eng. 39(2), 967–976 (2014)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)Google Scholar
  8. 8.
    Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02, vol. 2, pp. 1671–1676. IEEE (2002)Google Scholar
  9. 9.
    Liu, H., Howely, E., Duggan, J.: Particle swarm optimisation with gradually increasing directed neighbourhoods. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 29–36. ACM (2011)Google Scholar
  10. 10.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  11. 11.
    Robinson, J., Rahmat-Samii, Y.: Particle swarm optimization in electromagnetics. IEEE Trans. Antennas Propag. 52(2), 397–407 (2004)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Selvakumar, A.I., Thanushkodi, K.: A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22(1), 42–51 (2007)CrossRefGoogle Scholar
  13. 13.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem defnitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL Report 2005005, 2005 (2005)Google Scholar
  14. 14.
    Xu, S., Rahmat-Samii, Y.: Boundary conditions in particle swarm optimization revisited. IEEE Trans. Antennas Propag. 55(3), 760–765 (2007)CrossRefGoogle Scholar
  15. 15.
    Yang, C., Simon, D.: A new particle swarm optimization technique. In: 18th International Conference on Systems Engineering, ICSEng 2005, pp. 164–169. IEEE (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Discipline of Information TechnologyNational University of Ireland GalwayGalwayIreland

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