Abstract
One of the biggest drawbacks of the original Particle Swarm Optimization is the premature convergence and fast loss of diversity in the population. In this paper, we propose and discuss a simple yet effective modification to help the PSO maintain diversity and avoid premature convergence. The particles are randomly attracted towards the border points of the search space. We use the CEC13 Benchmark function set to test the performance of proposed method and compare it to original PSO.
M. Pluhacek—This work was supported by Grant Agency of the Czech Repub-lic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2017/004.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
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. I. S (1998)
Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997)
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 1568-4946
Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Technical report 2 (2002)
Liang, J.J., Qu, B.-Y., Suganthan, P.N., Hernández-Díaz, A.G.: Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization. Technical report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical report, Nanyang Technological University, Singapore (2013)
Van Den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Inf. Sci. 176(8), 937–971 (2006)
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)
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
Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T. (2017). PSO with Attractive Search Space Border Points. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-59060-8_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59059-2
Online ISBN: 978-3-319-59060-8
eBook Packages: Computer ScienceComputer Science (R0)