Particle Swarm Optimization with Lévy Flight and Adaptive Polynomial Mutation in gbest Particle

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 235)


In this paper, particle swarm optimization (PSO) with levy flight is proposed. PSO is a population based global optimization algorithm has faster convergence but often gets stuck in local optima due to lack of diversity in the population. In the proposed method, levy flight is applied on a percentage of particles excluding global best particle to create diversity in population. Adaptive polynomial mutation is applied on global best (gbest) particle to get it out from the trap in local optima. The method is applied on well-known benchmark unconstrained functions and results are compares with classical PSO. Form the experimental result, it has been observed that the proposed method performs better than classical PSO.


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  1. 1.
    Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: International symposium on Micromachine and Human science, pp. 39–434 (1995)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: Particle Swarm optimization. In: IEEE International Joint Conference on Neural Networks, pp. 1942–1948. IEEE Press (1995)Google Scholar
  3. 3.
    Chena, M.-R., Lia, X., Zhanga, X., Lu, Y.-Z.: A novel particle swarm optimizer hybridized with external optimization. Applied Soft Computing, 367–373 (2010)Google Scholar
  4. 4.
    Pedersen, M.E.H.: Tuning & Simplifying Heuristically Optimization, Ph.D. thesis, school of Engineering Science, University of Southampton, England (2010)Google Scholar
  5. 5.
    Singh, N., Singh, S.B.: One Half Global Best Position Particle Swarm Optimization Algorithm. International Journal of Scientific & Engineering Research 2(8), 1–10 (2012)Google Scholar
  6. 6.
    Deep, K., Bansal, J.C.: Mean Particle Swarm Optimization for function optimization. International Journal of Computational Intelligence studies 1(1), 72–92 (2009)MathSciNetGoogle Scholar
  7. 7.
    Wang, H., Liu, Y., Li, C.H., Zeng, S.Y.: A hybrid particle swarm algorithm with Cauchy mutation. In: Proc. of IEEE Swarm Intelligence Symposium, pp. 356–360 (2007)Google Scholar
  8. 8.
    Higashi, N., lba, H.: Particle Swarm Optimization with Gaussian Mutation. In: Proc. IEEE Swarm Intelligence Symposium, Indianapolis, pp. 72–79 (2003)Google Scholar
  9. 9.
    Wu, X., Zhong, M.: Particle Swarm Optimization Based on Power Mutation. In: ISECS International Colloquium on Computing, Communication, Control, and Management (2009)Google Scholar
  10. 10.
    Tang, J., Zhao, X.: Particle Swarm Optimization with Adaptive Mutation. In: WASE International Conference on Information Engineering (2009)Google Scholar
  11. 11.
    Si, T., Jana, N.D., Sil, J.: Particle Swarm Optimization with Adaptive Polynomial Mutation. In: Proc. World Congress on Information and Communication Technologies (WICT 2011), Mumbai, India, pp. 143–147 (2011)Google Scholar
  12. 12.
    Jana, N.D., Si, T., Sil, J.: Particle Swarm Optimization with Adaptive Mutation in Local Best of Particles. In: International Proceedings of Computer Science and Information Technology (IPCSIT 2012), Singapore, pp. 10–14 (March 2012)Google Scholar
  13. 13.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 82–102 (1999); Unger, R.: The genetic algorithm approach to protein structure prediction. Structure and Bonding 110, 153–175 (2004)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information TechnologyNational Institute of TechnologyDurgapurIndia
  2. 2.Department of Computer Science and TechnologyBESUShibpurIndia

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