Abstract
Based on a brief introduction to the principle of particle swarm optimization (PSO) and different improved methods of PSO at home and abroad, the particle swarm optimization algorithm is improved for the shortcomings of particle swarm optimization. This paper selects the particle velocity formula with a learning model, which has good exploration ability and can effectively avoid the algorithm falling into local optimum, and recon figure some parameters of the algorithm. A PSO with two crossover operations (PSOCO) is put forward, and the next research direction of PSOCO algorithm is given.
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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).
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Chang, C., Wu, X. (2020). An Improved Particle Swarm Optimization Algorithm. 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_195
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DOI: https://doi.org/10.1007/978-3-030-15235-2_195
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