Abstract
Particle Learning Optimization (PSO) is a novel heuristic algorithm that has undergone decades of evolution. Social learning particle swarm optimization (SL-PSO) proposed by Cheng, Jin et al. in 2016 [1] remarkably improves the PSO algorithm by applying multi-swarm learning strategy. Nevertheless, randomness on setting inertia and choosing learning objects gives rise to an unbalanced emphasis on global search, and thus impairs convergence rate and exploitation ability. The proposed ISL-PSO algorithm strengthens global search capability through modelling selected learning mechanism, in which learning objects are selected through generated learning possibility subjected to Gauss distribution. Furthermore, ISL-PSO algorithm models condition-based attraction process, in which particles are attracted to the center by calculating transformed distance between particles and the center. By applying the strategies, ISL-PSO improves convergence speed and accuracy of the original algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cheng, R.: A social learning particle swarm optimization algorithm for scalable optimization. Inf. Sci. 291, 43–60 (2015)
Poli, R.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)
Dorigo, M.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC 1999 (Cat. No. 99TH8406), pp. 1470–1477, Washington, D.C., USA. IEEE (1999)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)
Beheshti, Z.: A review of population-based meta-heuristic algorithms. Int. J. Adv. Soft Comput. Appl. 5(1), 1–35 (2013)
Chen, F.: Tradeoff strategy between exploration and exploitation for PSO. In: 2011 Seventh International Conference on Natural Computation, pp. 1216–1222, Shanghai, China. IEEE (2011)
Wang, X.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognit. Lett. 28(4), 459–471 (2007)
Gaing, Z.L.: Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. Power Syst. 18(3), 1187–1195 (2003)
Ruiz-Cruz, R.: Particle swarm optimization for discrete-time inverse optimal control of a doubly fed induction generator. IEEE Trans. Cybern. 43(6), 1698–1709 (2013)
Nagesh, R., Raga, S., Mishra, S.: Design of an energy-efficient routing protocol using adaptive PSO technique in wireless sensor networks. In: Sridhar, V., Padma, M.C., Rao, K.A.R. (eds.) Emerging Research in Electronics, Computer Science and Technology. LNEE, vol. 545, pp. 1039–1053. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-5802-9_90
Yang, Y.: A comparative study on feature selection in text categorization. In: ICML, Nashville, Tennessee, USA, no. 412–420, p. 35. Morgan Kaufmann (1997)
Bansal, J.C., Singh, P.K.: Inertia weight strategies in particle swarm optimization. In: 2011 Third World Congress on Nature and Biologically Inspired Computing, Salamanca, Spain, pp. 633–640. IEEE (2011)
Zhan, Z.H.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(6), 1362–1381 (2009)
Robinson, J., Sinton, S., Rahmat-Samii, Y.: Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No. 02CH37313), San Antonio, TX, USA, pp. 314–317. IEEE (2002)
Juang, C.F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(2), 997–1006 (2004)
Kao, Y.T.: A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl. Soft Comput. 8(2), 849–857 (2008)
Shelokar, P.S.: Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Appl. Math. Comput. 188(1), 129–142 (2007)
Abd-Elazim, S.M., Ali, E.S.: A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Int. J. Electr. Power Energy Syst. 46, 334–341 (2013)
Van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
Liang, J.J.: Dynamic multi-swarm particle swarm optimizer. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, Pasadena, CA, USA, pp. 124–129. IEEE (2005)
Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), Seoul, South Korea, pp. 81–86. IEEE (2001)
Lefebvre, L.: Culturally-transmitted feeding behavior in primates: evidence for accelerating learning rates. Primates 36(2), 227–239 (1995)
Gumaida, B.F.: A hybrid particle swarm optimization with a variable neighborhood search for the localization enhancement in wireless sensor networks. Appl. Intell. 49, 1–19 (2019). https://doi.org/10.1007/s10489-019-01467-8
Zhang, X.: Differential mutation and novel social learning particle swarm optimization algorithm. Inf. Sci. 480, 109–129 (2019)
Zentall, T.R.: Imitation in animals: evidence, function, and mechanisms. Cybern. Syst. 32(1–2), 53–96 (2001)
Acknowledgements
Ming Chen and Haoruo Hu contributed equally to this paper and shared the first authorship. This work is partially supported by the Natural Science Foundation of Guangdong Province (2016A030310074), Project supported by Innovation and Entrepreneurship Research Center of Guangdong University Student (2018A073825), Research Cultivation Project from Shenzhen Institute of Information Technology (ZY201717) and Innovating and Upgrading Institute Project from Department of Education of Guangdong Province (2017GWTSCX038).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, H., Chen, M., Song, X., Chia, E.T., Tan, L. (2019). An Improved Social Learning Particle Swarm Optimization Algorithm with Selected Learning. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-26766-7_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26765-0
Online ISBN: 978-3-030-26766-7
eBook Packages: Computer ScienceComputer Science (R0)