Forecasting the dynamic of the COVID-19 pandemic by an adaptive Cauchy Quantum-Behaved Particle Swarm Optimization Algorithm
2nd International Conference on Digital Signal and Computer Communications, DSCC 2022
; 12306, 2022.
Article
in English
| Scopus | ID: covidwho-2019667
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. © 2022 SPIE.
component; COVID-19; Parameter estimation; Quantum-behaved particle swarm optimization; Particle swarm optimization (PSO); Modeling parameters; Parameters estimation; Particle swarm; Piece-wise; Quantum particle swarm optimization algorithm; Quantum-behaved Particle Swarm Optimization algorithms; Swarm optimization; Transmission model
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Digital Signal and Computer Communications, DSCC 2022
Year:
2022
Document Type:
Article
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