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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Amitava, C., Patrick, S.: Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput. Oper. Res. 33(3), 859–871 (2006)
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)
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)
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)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)
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)
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)
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)
Federico, M., Beata, W.: Particle swarm optimization (PSO) a tutorial. Chemometr. Intell. Lab. Syst. 149, 153–165 (2015)
Acknowledgments
This research is supported by the National Natural Science Foundation of China under Grant No. 61671041.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhou, Q., Lu, H., Shi, J., Mao, K., Ji, X. (2017). The Analysis of Strategy for the Boundary Restriction in Particle Swarm Optimization Algorithm. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-61824-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61823-4
Online ISBN: 978-3-319-61824-1
eBook Packages: Computer ScienceComputer Science (R0)