Forecasting the dynamic of the COVID-19 pandemic by an adaptive Cauchy quantum-behaved particle swarm optimization algorithm

Baoshan Ma; Jishuang Qi; Xiaoyu Hou; Yi Gong; Tong Xiong; Yuanze Fang

doi:10.1117/12.2641270

4 August 2022 Forecasting the dynamic of the COVID-19 pandemic by an adaptive Cauchy quantum-behaved particle swarm optimization algorithm

Baoshan Ma, Jishuang Qi, Xiaoyu Hou, Yi Gong, Tong Xiong, Yuanze Fang

Author Affiliations +

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 123061U (2022) https://doi.org/10.1117/12.2641270
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

Accurate identification of parameters is critical to the epidemiological utility of the results obtained from the COVID-19 transmission model. In order to optimize the model parameters, we propose an adaptive Cauchy quantum particle swarm optimization (QPSO) algorithm. We introduce a piecewise Cauchy mutation operator and the mutation probability is adjusted adaptively according to the fitness to enhance the global search ability of QPSO. The experimental results show that the improved QPSO algorithm has higher accuracy than original QPSO and PSO algorithms.

Citation Download Citation

Baoshan Ma, Jishuang Qi, Xiaoyu Hou, Yi Gong, Tong Xiong, and Yuanze Fang "Forecasting the dynamic of the COVID-19 pandemic by an adaptive Cauchy quantum-behaved particle swarm optimization algorithm", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 123061U (4 August 2022); https://doi.org/10.1117/12.2641270

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