Bare Bones Particle Swarms with Jumps

  • Mohammad Majid al-Rifaie
  • Tim Blackwell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7461)


Bare Bones PSO was proposed by Kennedy as a model of PSO dynamics. Dependence on velocity is replaced by sampling from a Gaussian distribution. Although Kennedy’s original formulation is not competitive to standard PSO, the addition of a component-wise jumping mechanism, and a tuning of the standard deviation, can produce a comparable optimisation algorithm. This algorithm, Bare Bones with Jumps, exists in a variety of formulations. Two particular models are empirically examined in this paper and comparisons are made to canonical PSO and standard Bare Bones.


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  1. 1.
    Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in amultidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  2. 2.
    Yang, Y., Kamel, M.: Clustering ensemble using swarm intelligence. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003, pp. 65–71. IEEE (2003)Google Scholar
  3. 3.
    van den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Information Sciences 176(8), 937–971 (2006)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Kennedy, J.: Bare bones particle swarms. In: Proceedings of Swarm Intelligence Symposium (SIS 2003), pp. 80–87. IEEE (2003)Google Scholar
  5. 5.
    Blackwell, T.: A study of collapse in bare bones particle swarm optimisation. IEEE Transactions on Evolutionary Computing (99) (2012)Google Scholar
  6. 6.
    Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85(6), 317–325 (2003)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley (2006)Google Scholar
  8. 8.
    Jones, D.R., Perttunen, C.D., Stuckman, B.E.: Lipschitzian optimization without the lipschitz constant. J. Optim. Theory Appl. 79(1), 157–181 (1993)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Jong, K.A.D.: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor, MI, USA (1975)Google Scholar
  10. 10.
    al-Rifaie, M.M., Bishop, M., Blackwell, T.: Resource allocation and dispensation impact of stochastic diffusion search on differential evolution algorithm. In: Nature Inspired Cooperative Strategies for Optimisation (NICSO 2011). Springer (2011)Google Scholar
  11. 11.
    Gehlhaar, D., Fogel, D.: Tuning evolutionary programming for conformationally flexible molecular docking. In: Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming, pp. 419–429 (1996)Google Scholar
  12. 12.
    Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Proc. of the Swarm Intelligence Symposium, Honolulu, Hawaii, USA, pp. 120–127. IEEE (2007)Google Scholar
  13. 13.
    Clerc, M.: From theory to practice in particle swarm optimization. In: Handbook of Swarm Intelligence, pp. 3–36 (2010)Google Scholar
  14. 14.
    Richer, T., Blackwell, T.: The lévy particle swarm. In: IEEE Congress on Evolutionary Computation, pp. 3150–3157 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mohammad Majid al-Rifaie
    • 1
  • Tim Blackwell
    • 1
  1. 1.GoldsmithsUniversity of LondonLondonUK

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