Abstract
Particle swarm optimization (PSO) is a widely used heuristic algorithm. However, canonical PSO may lead to premature convergence. To solve this problem, researchers try to hybridize PSO with genetic algorithm (GA) which facilitates global effectiveness. One of the successful algorithms is genetic learning PSO (GL-PSO). However, we find that the selection in GL-PSO reduce the diversity of particles. It may lead premature convergence in some test functions. To solve this problem, we figure out a genetic learning particle swarm optimization with diverse selection (GL-PSODS). We test our proposed algorithm in test functions of CEC2014. Our experiments show that GL-PSODS has an improvement in some test functions compared to PSO and GL-PSO.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alrashidi, M.R., El-Hawary, M.E.: A survey of particle swarm optimization applications in electric power systems. IEEE Press (2009)
Arumugam, M.S., Rao, M.V.C.: On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Appl. Soft Comput. 8(1), 324–336 (2008)
Cai, Y., Chen, Z., Li, J., Li, Q., Min, H.: An adaptive particle swarm optimization algorithm for distributed search and collective cleanup in complex environment. Int. J. Distrib. Sens. Netw. 2013(4), 1–9 (2013)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: International Symposium on MICRO Machine and Human Science, pp. 39–43 (2002)
Frans, V.D.B., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
Gong, Y.J., Li, J.J., Zhou, Y., Li, Y., Chung, H.S., Shi, Y.H., Zhang, J.: Genetic learning particle swarm optimization. IEEE Trans. Cybern. 46(10), 2277 (2016)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35(6), 1272–1282 (2005)
Kao, Y.T., Zahara, E.: A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl. Soft Comput. 8(2), 849–857 (2008)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (2002)
Kulkarni, R.V., Venayagamoorthy, G.K.: Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans. Syst. Man Cybern. Part C 41(2), 262–267 (2011)
Lane, M.C., Xue, B., Liu, I., Zhang, M.: Particle swarm optimisation and statistical clustering for feature selection. In: Cranefield, S., Nayak, A. (eds.) AI 2013. LNCS (LNAI), vol. 8272, pp. 214–220. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03680-9_23
Ling, S.H., Iu, H.H., Chan, K.Y., Lam, H.K., Yeung, B.C., Leung, F.H.: Hybrid particle swarm optimization with wavelet mutation and its industrial applications. IEEE Trans. Syst. Man Cybern. Part B Cybern. A Publ. IEEE Syst. Man Cybern. Soc. 38(3), 743 (2008)
Ruiz-Cruz, R., Sanchez, E.N., Ornelas-Tellez, F., Loukianov, A.G., Harley, R.G.: Particle swarm optimization for discrete-time inverse optimal control of a doubly fed induction generator. IEEE Trans. Cybern. 43(6), 1698–1709 (2013)
Shi, X.H., Liang, Y.C., Lee, H.P., Lu, C., Wang, L.M.: An improved GA and a novel PSO-GA-based hybrid algorithm. Inf. Process. Lett. 93(5), 255–261 (2005)
Valdez, F., Melin, P., Mendoza, O.: A new evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms: the case of neural networks optimization, vol. 574, pp. 1536–1543 (2008)
Acknowledgment
This work is supported by the Fundamental Research Funds for the Central Universities, SCUT (NO. 2017ZD048,2015ZM136), Tiptop Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2015TQ01X633), Science and Technology Planning Project of Guangdong Province, China (No. 2016A030310423), Science and Technology Program of Guangzhou (International Science and Technology Cooperation Program No. 201704030076) and Science and Technology Planning Major Project of Guangdong Province (No. 2015A070711001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ren, D., Cai, Y., Huang, H. (2018). Genetic Learning Particle Swarm Optimization with Diverse Selection. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_83
Download citation
DOI: https://doi.org/10.1007/978-3-319-95957-3_83
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95956-6
Online ISBN: 978-3-319-95957-3
eBook Packages: Computer ScienceComputer Science (R0)