Advertisement

Small World Particle Swarm Optimizer for Global Optimization Problems

  • Megha Vora
  • T. T. Mirnalinee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

Particle swarm is a stochastic optimization paradigm inspired by the concepts of social psychology and artificial intelligence. Interrelationship between individuals in a swarm is defined by the population topology, which can be depicted as a network model. Regular networks are highly clustered but the characteristic path length grows linearly with the increase in number of vertices. On the contrary, random networks are not highly clustered but they have small characteristic path length. Small world network have a distinctive combination of regular and random networks i.e., highly clustered and small characteristic path length. This paper takes forward the concept of incorporating small world theory in the Particle Swarm Optimization (PSO) framework. Efficiency of the proposed methodology is tested by applying it on twelve standard benchmark functions. Results obtained are compared with other PSO variants. Comparative study demonstrates the effectiveness of the proposed approach.

Keywords

Particle Swarm Optimization Particle Swarm Small World Inertia Weight Benchmark Function 
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.

References

  1. 1.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Suganthan, P.N.: Particle swarm optimizer with neighborhood operator. In: Congress on Evolutionary Computation, pp. 1958–1962 (1999)Google Scholar
  3. 3.
    Saxena, A.K., Vora, M.: Novel approach for the use of small world theory in particle swarm optimization. In: 16th International Conference on Advanced Computing and Communications, pp. 363–366. IEEE (2008)Google Scholar
  4. 4.
    Li, C., Yang, S., Nguyen, T.: A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B 42(3), 627–646 (2012)CrossRefGoogle Scholar
  5. 5.
    Mendes, R.: Population Topologies and Their Influence in Particle Swarm Performance. PhD thesis, University of Minho (2004)Google Scholar
  6. 6.
    Kennedy, J.: Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. In: IEEE CEC, pp. 1931–1938 (1999)Google Scholar
  7. 7.
    Kleinberg, J.: The small-world phenomenon: An algorithmic perspective. Technical report, Cornell University Ithaca, NY, USA (1999)Google Scholar
  8. 8.
    Milgram, S.: The small world problem. Psychology Today 2, 60–67 (1967)Google Scholar
  9. 9.
    Watts, D., Strogatz, S.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  10. 10.
    Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann (2001)Google Scholar
  11. 11.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Technical report (2005)Google Scholar
  12. 12.
    van den Bergh, F.: An Analysis of Particle Swarm Optimizer. PhD thesis, Department of Computer Science, University of Petoria, South Africa (2002)Google Scholar
  13. 13.
    Seltman, H.J.: Experimental Design and analysis. Carnegie Mellon University (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Megha Vora
    • 1
  • T. T. Mirnalinee
    • 1
  1. 1.Department of Computer Science and EngineeringS.S.N College of Engineering, Anna UniversityChennaiIndia

Personalised recommendations