Abstract
Traditional particle swarm optimization algorithm has some disadvantages, such as slow convergence speed and easy to fall into local extremes. In order to improve the performance, an improved adaptive particle swarm optimization algorithm with a two-way learning method is proposed. First, the algorithm adaptively adjusts the algorithm according to the iteration periods of the optimization process. Specifically, the inertia weight and the value of the learning factor are changed nonlinearly, so as to better balance the search behavior of the particles in the group; Second, the idea of beetle search is introduced into the particle swarm algorithm to form a new two-way learning mechanism, which overcomes the limitations of the traditional particle swarm algorithm and help to increase the diversity of the population. In this way, the search scope is expanded, and the search accuracy of the algorithm is enhanced. Finally, the simulation is carried out on several multi-dimensional functions, and compared with other two related algorithms. The experimental results show that under the same experimental conditions, the improved algorithm has obvious advantages in optimization ability and convergence speed.
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
Similar content being viewed by others
References
Kennedy, J., Eberhartrc.: Particle swarm optimization. IEEE International Conference on Neural Networks, Perth, Australia: 1942–19489 (1995)
Hewahi, N.: A hybrid approach based on genetic algorithm and particle swarm optimization to improve neural network classification. J. Inf. Technol. Res. 10(3), 48–68 (2017)
Wu, L., Cheng, W., Wang, X.: Application of simulated annealing particle swarm algorithm to optimize two-dimensional entropy image segmentation. Comput. Eng. Design 040(009), 2544–2551 (2019)
Zhang, X., Kang, Q., **a, W., et al.: Particle swarm optimization algorithm based on cross-reverse learning and cogranular social learning. J. Comput. Appl. 37(011), 3194–3200 (2017)
Li, L., Zhang, X.: A new adaptive inertial weight chaotic PSO algorithm. Comput. Eng. Appl. 054(009), 139–144 (2018)
Tong, Q., Zhao, Q., Li, M.: Particle swarm optimization algorithm based on adaptive dynamic change. Microelectron. Comput. 36(2), 6–10
Wu, Q., Zhang, W., Yang, H. et al.: Application of particle swarm optimization based on longhorn whisker algorithm in PID parameter tuning. Electron. Sci. Technol. 33, 369(06), 22–27 (2020)
Tian, D., Zhao, T.: Adaptive particle swarm optimization algorithm based on Sigmoid inertia weights. J. Comput. Appl. 28(12), 3058–3061 (2008)
Xu, S., **a, W., Dai, A.: A particle swarm optimization algorithm with improved learning factor. Inf. Secur. Technol. 003(007), 17–19 (2012)
Ding, X., Wu, X., Cheng, H.: Research on wireless sensor network coverage problem based on improved particle swarm algorithm and feature point set. Chin. J. Electron. 44(4), 967–973 (2016)
Zhang, X., Wang, X., Kang, Q., Cheng, J.: Differential mutation and novel social learning particle swarm optimization algorithm. Inf. Sci. (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y., Qian, Q., Feng, Y., Fu, Y. (2022). Improved Adaptive Particle Swarm Optimization Algorithm with a Two-Way Learning Method. In: Jain, L.C., Kountchev, R., Hu, B., Kountcheva, R. (eds) Smart Communications, Intelligent Algorithms and Interactive Methods. Smart Innovation, Systems and Technologies, vol 257. Springer, Singapore. https://doi.org/10.1007/978-981-16-5164-9_21
Download citation
DOI: https://doi.org/10.1007/978-981-16-5164-9_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5163-2
Online ISBN: 978-981-16-5164-9
eBook Packages: EngineeringEngineering (R0)