Abstract
Although the particle swarm optimization algorithm has simple principle, few parameters and easy implementation, the particle swarm optimization algorithm is easy to fall into local optimum on multi-mode function and the local search ability is relatively weak. In this paper, the improvement of these two defects is carried out. The particle motion formula with learning model is added, and the generation strategy of a guided vector is added to improve the particle swarm optimization algorithm. The improved algorithm has a two-layer structure, and finally the research direction is prospected.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang Q, Liu W, Meng X, et al (2017) Vector coevolving particle swarm optimization algorithm. Inf Sci 394(C):273–298
Li R, Shen Y, Liu J (2015) Improved adaptive particle swarm optimization algorithm. Comput Eng Appl 51(13):31–36. (in Chinese)
Wang H, Sun H, Li C et al (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci Int J 223(2):119–135
Fei Yu, Yuanxiang L, Bo W et al (2014) Application of lens imaging inverse learning strategy in particle swarm optimization algorithm. Chin J Electron 42(2):230–235
Zhou J, Fang W, Wu X, et al (2016) An opposition-based learning competitive particle swarm optimizer. In: Evolutionary computation. IEEE
Wang Y, Li B, Weise T et al (2010) Self-adaptive learning based particle swarm optimization. Inf Sci 81(20):4515–4538
Zhang WJ, Xie XF (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: 2003 IEEE international conference on systems, man and cybernetics, vol 4. IEEE, pp 3816–3821
Moore PW, Venayagamoorthy GK (2006) Evolving digital circuits using hybrid particle swarm optimization and differential evolution. Int J Neural Syst 16(03):163–177
Epitropakis MG, Plagianakos VP, Vrahatis MN (2012) Evolving cognitive and social experience in particle swarm optimization through differential evolution: a hybrid approach. Inf Sci 216:50–92
Zhang M, Zhang W, Sun Y (2009) Chaotic co-evolutionary algorithm based on differential evolution and particle swarm optimization. In: 2009 IEEE international conference on automation and logistics, ICAL 2009. IEEE, pp 885–889
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Project No. 51678375), Natural Science Foundation of Liaoning Province (Project No. 2015020603), and the basic scientific research project of Liaoning Higher Education (Project No. LJZ2017009).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chang, C., Wu, X. (2020). Research on Improvement of Particle Swarm Optimization. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_174
Download citation
DOI: https://doi.org/10.1007/978-3-030-15235-2_174
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15234-5
Online ISBN: 978-3-030-15235-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)