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The Analysis of Strategy for the Boundary Restriction in Particle Swarm Optimization Algorithm

  • Qianlin Zhou
  • Hui LuEmail author
  • Jinhua Shi
  • Kefei Mao
  • Xiaonan Ji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)

Abstract

Particle swarm optimization has been applied to solve many optimization problems because of its simplicity and fast convergence performance. In order to avoid precocious convergence and further improve the ability of exploration and exploitation, many researchers modify the parameters and the topological structure of the algorithm. However, the boundary restriction strategy to prevent the particles from flying beyond the search space is rarely discussed. In this paper, we investigate the problems of the strategy that putting the particles beyond the search space on the boundary. The strategy may cause PSO to get stuck in the local optimal solutions and even the results cannot reflect the real performance of PSO. In addition, we also compare the strategy with the random updating strategy. The experiment results prove that the strategy that putting the particles beyond the search space on the boundary is unreasonable, and the random updating strategy is more effective.

Keywords

Particle swarm optimization (PSO) Boundary restriction strategy Random updating strategy 

Notes

Acknowledgments

This research is supported by the National Natural Science Foundation of China under Grant No. 61671041.

References

  1. 1.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the Feedback Mechanism IEEE International Conference on Neural Networks. IEEE Service Center, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, Anchorage, AK, pp. 69–73 (1998)Google Scholar
  3. 3.
    Shi, Y.H., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: 7th International Conference, EP98 San Diego, California, USA, vol. 1447, pp. 591–600 (1998)Google Scholar
  4. 4.
    Amitava, C., Patrick, S.: Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput. Oper. Res. 33(3), 859–871 (2006)CrossRefGoogle Scholar
  5. 5.
    Chen H.H., Li G.Q., Liao H.l.: A self-adaptive improved particle swarm optimization algorithm and its application in available transfer capability calculation. In: 2009 Fifth International Conference on Natural Computation, Tianjin, pp. 200–205 (2009)Google Scholar
  6. 6.
    Ratnaweera, A., Halgamuge, S., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)CrossRefGoogle Scholar
  7. 7.
    Suganthan P.N.: Particle swarm optimiser with neighbourhood operator. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 99, Washington, DC, vol. 3, p. 1962 (1999)Google Scholar
  8. 8.
    Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)CrossRefGoogle Scholar
  9. 9.
    Lvbjerg M., Rasmussen T.K., Krink T.: Hybrid particle swarm optimizer with breeding and subpopulations. In: Proceedings of the Third Genetic and Evolutionary Computation Conference, vol. 1, pp. 469–476 (2001)Google Scholar
  10. 10.
    Higashi N., Iba H.: Particle swarm optimization with Gaussian mutation. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003, pp. 72–79 (2003)Google Scholar
  11. 11.
    Feng M., Pan H.: A Modified PSO Algorithm Based on Cache Replacement Algorithm. In: 2014 Tenth International Conference on Computational Intelligence and Security (CIS), Kunming, pp. 558–562 (2014)Google Scholar
  12. 12.
    Federico, M., Beata, W.: Particle swarm optimization (PSO) a tutorial. Chemometr. Intell. Lab. Syst. 149, 153–165 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qianlin Zhou
    • 1
  • Hui Lu
    • 1
    Email author
  • Jinhua Shi
    • 1
  • Kefei Mao
    • 1
  • Xiaonan Ji
    • 1
  1. 1.School of Electronic and Information EngineeringBeihang UniversityBeijingChina

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