Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Particle Swarm Optimization

Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_630
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The Canonical Particle Swarm

The particle swarm is a population-based stochastic algorithm for optimization which is based on social–psychological principles. Unlike evolutionary algorithms, the particle swarm does not use selection; typically, all population members survive from the beginning of a trial until the end. Their interactions result in iterative improvement of the quality of problem solutions over time.

A numerical vector of D dimensions, usually randomly initialized in a search space, is conceptualized as a point in a high-dimensional Cartesian coordinate system. Because it moves around the space testing new parameter values, the point is well described as a particle. Because a number of them (usually 10 < N < 100) perform this behavior simultaneously, and because they tend to cluster together in optimal regions of the search space, they are referred to as a particle swarm.

Besides moving in a (usually) Euclidean problem space, particles are typically enmeshed in a...

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Recommended Reading

  1. Abelson, R. P., Aronson, E., McGuire, W. J., Newcomb, T. M., Rosenberg, M. J., & Tannenbaum, R. H. (Eds.), (1968). Theories of cognitive consistency: A sourcebook. Chicago: Rand McNally.Google Scholar
  2. Clerc, M. (2006). Particle swarm optimization. London: Hermes Science Publications.zbMATHCrossRefGoogle Scholar
  3. Clerc, M., & Kennedy, J. (2002). The particle swarm: Explosion, stability, and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 58–73.CrossRefGoogle Scholar
  4. Eberhart, R.C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the 6th international symposium on micro machine and human science, (Nagoya, Japan) (pp. 39–43). Piscataway, NJ: IEEE Service Center.Google Scholar
  5. Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.Google Scholar
  6. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.CrossRefGoogle Scholar
  7. Janson, S., & Middendorf, M. (2005). A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man, and Cybernatics – Part B: Cybernatics, 35(6), 1272–1282.Google Scholar
  8. Kennedy, J. (1998). The behavior of particles. In V. W. Porto, N. Saravanan, D. Waagen, & A. E. Eiben (Eds.), Evolutionary programming VII. Proceedings of the 7th annual conference on evolutionary programming.Google Scholar
  9. Kennedy, J. (2003). Bare bones particle swarms. In Proceedings of the IEEE swarm intelligence symposium (pp. 80–87). Indianapolis, IN.Google Scholar
  10. Kennedy, J. (2005). Dynamic-probabilistic particle swarms. In Proceedings of the genetic and evolutionary computation conference (GECCO-2005) (pp. 201–207). Washington, DC.Google Scholar
  11. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE international conference on neural networks (Perth, Australia) (pp. 1942–1948). Piscataway, NJ: IEEE Service Center.Google Scholar
  12. Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In Proceedings of the 1997 conference on systems, man, and cybernetics (pp. 4104–4109). Piscataway, NJ: IEEE Service Center.Google Scholar
  13. Krohling, R. A. (2004). Gaussian Swarm. A novel particle swarm optimization algorithm. Proceedings of the 2004 IEEE conference on cybernetics and intelligent systems (vol. 1, pp. 372–376).Google Scholar
  14. Mendes, R. (2004). Population topologies and their influence in particle swarm performance. Doctoral thesis, Escola de Engenharia, Universidade do Minho, Portugal.Google Scholar
  15. Nowak, A., Szamrej, J., & Latané, B. (1990). From private attitude to public opinion: A dynamic theory of social impact. Psychological Review, 97, 362–376.CrossRefGoogle Scholar
  16. Owen, A., & Harvey, I. (2007). Adapting particle swarm optimisation for fitness landscapes with neutrality. In Proceedings of the 2007 IEEE swarm intelligence symposium (pp. 258–265). Honolulu, HI: IEEE Press.Google Scholar
  17. Ozcan, E., & Mohan, C. K. (1999). Particle swarm optimization: Surfing the waves. In Proceedings of the congress on evolutionary computation, Mayflower hotel, Washington D.C. (pp. 1939–1944). Piscataway, NJ: IEEE Service Center.Google Scholar
  18. Peña, J., Upegui, A., & Eduardo Sanchez, E. (2006). Particle swarm optimization with discrete recombination: An online optimizer for evolvable hardware. In Proceedings of the 1st NASA/ESA conference on adaptive hardware and systems (AHS-2006), Istanbul, Turkey (pp. 163–170). Piscataway, NJ: IEEE Service Center.Google Scholar
  19. Richer, T. J., & Blackwell, T. M. (2006). The Levy particle swarm. In Proceedings of the 2006 congress on evolutionary computation (CEC-2006). Piscataway, NJ: IEEE Service Center.Google Scholar
  20. Shi, Y., & Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In Evolutionary Programming VII: Proc. EP98 (pp. 591–600). New York: Springer.Google Scholar
  21. Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group, (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1, Foundations (pp. 194–281). Cambridge, MA: MIT Press.Google Scholar
  22. Suganthan, P. N. (1999). Particle swarm optimisation with a neighbourhood operator. In Proceedings of congress on evolutionary computation. Washington DC, USA.Google Scholar
  23. Thagard, P. (2000). Coherence in thought and action. Cambridge, MA: MIT Press.Google Scholar

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.U.S. Bureau of Labor StatisticsWashingtonUSA