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Particle Swarm Optimization with Single Particle Repulsivity for Multi-modal Optimization

  • Michal PluhacekEmail author
  • Roman Senkerik
  • Adam Viktorin
  • Tomas Kadavy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

This work presents a simple but effective modification of the velocity updating formula in the Particle Swarm Optimization algorithm to improve the performance of the algorithm on multi-modal problems. The well-known issue of premature swarm convergence is addressed by a repulsive mechanism implemented on a single-particle level where each particle in the population is partially repulsed from a different particle. This mechanism manages to prolong the exploration phase and helps to avoid many local optima. The method is tested on well-known and typically used benchmark functions, and the results are further tested for statistical significance.

Keywords

Particle Swarm Optimization PSO Convergence Repulsivity 

References

  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)Google Scholar
  3. 3.
    Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997)Google Scholar
  4. 4.
    Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011). ISSN 1568-4946CrossRefGoogle Scholar
  5. 5.
    Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, USA, pp. 84–88 (2000)Google Scholar
  6. 6.
    Van Den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Inf. Sci. 176(8), 937–971 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Technical Report, vol. 2, p. 2002. Department of Computer Science, University of Aarhus, Aarhus, Denmark (2002)Google Scholar
  8. 8.
    Han, F., Liu, Q.: An improved hybrid PSO based on ARPSO and the Quasi-Newton method. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9140, pp. 460–467. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-20466-6_48CrossRefGoogle Scholar
  9. 9.
    Engelbrecht, A.P.: Particle swarm optimization: iteration strategies revisited. In: 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, Ipojuca, pp. 119–123 (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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