Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm
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 Aqu...
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| Format: | Article |
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MDPI AG
2021-10-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/5/4/190 |
| _version_ | 1797504478637719552 |
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| author | Lin Ma Jun Li Ye Zhao |
| author_facet | Lin Ma Jun Li Ye Zhao |
| author_sort | Lin Ma |
| collection | DOAJ |
| description | 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, <i>b</i>, <i>c</i>) 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, <i>b</i>, <i>c</i>). At the end of this paper, the forecast data of China’s rural population from 2020 to 2024 are given for reference. |
| first_indexed | 2024-03-10T04:05:09Z |
| format | Article |
| id | doaj.art-36137f4afc51461b9cb9152e72698f1c |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-10T04:05:09Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-36137f4afc51461b9cb9152e72698f1c2023-11-23T08:23:31ZengMDPI AGFractal and Fractional2504-31102021-10-015419010.3390/fractalfract5040190Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer AlgorithmLin Ma0Jun Li1Ye Zhao2School of Urban Design, Wuhan University, Wuhan 430072, ChinaSchool of Urban Design, Wuhan University, Wuhan 430072, ChinaAcademy of Architecture, Chang’an University, Xi’an 710061, ChinaRural 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, <i>b</i>, <i>c</i>) 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, <i>b</i>, <i>c</i>). At the end of this paper, the forecast data of China’s rural population from 2020 to 2024 are given for reference.https://www.mdpi.com/2504-3110/5/4/190rural population forecastingAquila Optimizerquasi-opposition learning strategywavelet mutation strategyCFANGBM(1, 1, <i>b</i>, <i>c</i>) |
| spellingShingle | Lin Ma Jun Li Ye Zhao Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm Fractal and Fractional rural population forecasting Aquila Optimizer quasi-opposition learning strategy wavelet mutation strategy CFANGBM(1, 1, <i>b</i>, <i>c</i>) |
| title | Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm |
| title_full | Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm |
| title_fullStr | Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm |
| title_full_unstemmed | Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm |
| title_short | Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm |
| title_sort | population forecast of china s rural community based on cfangbm and improved aquila optimizer algorithm |
| topic | rural population forecasting Aquila Optimizer quasi-opposition learning strategy wavelet mutation strategy CFANGBM(1, 1, <i>b</i>, <i>c</i>) |
| url | https://www.mdpi.com/2504-3110/5/4/190 |
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