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Adaptive Non-uniform Distribution of Quantum Particles in mQSO

  • Krzysztof Trojanowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

This paper studies properties of quantum particles rules of movement in particle swarm optimization (PSO) for non-stationary optimization tasks. A multi-swarm approach based on two types of particles: neutral and quantum ones is a framework of the experimental research. A new method of generation of new location candidates for quantum particles is proposed. Then a set of experiments is performed where this method is verified. The test-cases represent different situations which can occur in the search process concerning different numbers of moving peaks respectively to the number of sub-swarms. To obtain the requested circumstances in the testing environment the number of sub-swarms is fixed. The results show high efficiency and robustness of the proposed method in all of the tested variants.

Keywords

Search Space Neutral Particle Quantum Particle Location Candidate Base Version 
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.

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References

  1. 1.
    Blackwell, T.: Particle swarm optimization in dynamic environments. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol. 51, pp. 29–49. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Tr. Evolutionary Computation 10(4), 459–472 (2006)CrossRefGoogle Scholar
  4. 4.
    Li, X.: Adaptively choosing neighborhood bests in a particle swarm optimizer for multimodal function optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 105–116. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Li, X., Branke, J., Blackwell, T.: Particle swarm with speciation and adaptation in a dynamic environment. In: GECCO 2006: Proc. Conf. on Genetic and Evolutionary Computation, pp. 51–58. ACM Press, New York (2006)Google Scholar
  6. 6.
    Parrot, D., Li, X.: Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput. 10(4), 440–458 (2006)CrossRefGoogle Scholar
  7. 7.
    Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space. IEEE Tr. Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  8. 8.
    Kennedy, J.: Bare bones particle swarms. In: Proc. of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), pp. 80–87. IEEE Press, Los Alamitos (2003)Google Scholar
  9. 9.
    Trojanowski, K.: Non-uniform distributions of quantum particles in multi-swarm optimization for dynamic tasks. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part I. LNCS, vol. 5101, pp. 843–852. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Chambers, J.M., Mallows, C.L., Stuck, B.W.: A method for simulating stable random variables. J. Amer. Statist. Assoc. 71(354), 340–344 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Branke, J.: Memory enhanced evolutionary algorithm for changing optimization problems. In: Proc. of the Congress on Evolutionary Computation, vol. 3, pp. 1875–1882. IEEE Press, Piscataway (1999)Google Scholar
  12. 12.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Branke, J.: The moving peaks benchmark, http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/movpeaks/

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Krzysztof Trojanowski
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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