Abstract
Rural community population forecasting has important guiding significance to rural construction and development. In this study, a novel grey Bernoulli model combined with an improved Aquila Optimizer (IAO) was used to forecast rural community population in China. Firstly, this study improved the Aquila Optimizer by combining quasi-opposition learning strategy and wavelet mutation strategy, and proposed the new IAO algorithm. By comparing with other algorithms on CEC2017 test functions, the proposed IAO algorithm has the advantages of faster convergence speed and higher convergence accuracy. Secondly, based on the data of China’s rural community population from 1990 to 2019, a consistent fractional accumulation nonhomogeneous grey Bernoulli model called CFANGBM(1, 1, b, c) was established for rural population forecasting. The proposed IAO algorithm was used to optimize the parameters of the model, and then the rural population of China was predicted. Four error measures were used to evaluate the model, and by comparing with other forecasting models, the experimental results show that the proposed model had the smallest error between the forecasted value and the real value, which illustrates the effectiveness of using the IAO algorithm to solve CFANGBM(1, 1, b, c). At the end of this paper, the forecast data of China’s rural population from 2020 to 2024 are given for reference.
Highlights
Since the 1990s, China’s rural areas have experienced a drastic change, and the decline of rural areas is an indisputable objective fact
Four error measures were used to evaluate the model, and by comparing with other forecasting models, the experimental results show that the proposed model had the smallest error between the forecasted value and the real value, which illustrates the effectiveness of using the improved Aquila Optimizer (IAO) algorithm to solve CFANGBM(1, 1, b, c)
The proposed IAO algorithm was used to optimize the parameters of the forecasting model CFANGBM(1, 1, b, c), and to predict China’s rural population
Summary
Since the 1990s, China’s rural areas have experienced a drastic change, and the decline of rural areas is an indisputable objective fact. This study aimed to establish a prediction model to more accurately predict China’s rural population. The proposed improved Aquila Optimizer algorithm was used to solve the Chinese rural population forecasting model (CFANGBM(1, 1, b, c)). An improved Aquila Optimizer (namely, IAO) was proposed, which combines quasi-opposition learning and wavelet mutation strategy to improve the solution accuracy and convergence speed of the algorithm. A consistent fractional accumulation nonhomogeneous grey Bernoulli model named the CFANGBM(1, 1, b, c) for predicting rural population in China was established. The fitting error of the CFANGBM(1, 1, b, c) on population data was compared with other grey prediction models: GM(1, 1), DGM(1, 1), TRGM, and FTDGM.
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