Abstract:
Flower Pollination Algorithm (FPA) is one of the
popular heuristic algorithms that model pollination in the
natural environment. Since 2012, it has been used in the solution
of many difficult real world problems and successful results have
been achieved. In this study, FPA is utilized for the training of
neural network to predict number of COVID-19 cases. Namely, a
model based on FPA and neural network (FPA_NN) is proposed.
Within the scope of application, the data belonging to Turkey are
estimated using the proposed model. A data set is created with
the data between 1 April 2020 and 15 September 2020. A time
series is created with these data and the nonlinear dynamic
systems are obtained to model the problem. In order to
determine the performance of the proposed model, RMSE (root
mean square error) are used. The output graphs of the results are
also examined in detail. The results are compared with neural
network approaches based on PSO and HS. The Wilcoxon signed
rank test is utilized to determine the significance of the results.
The results show that FPA is generally more effective than PSO
and HS to predict number of COVID-19 cases based on neural
network
