Abstract
In this paper, the optimal weighted combination model and fractional grey model are constructed. The coefficients of the optimal weighted combination model are determined by minimizing the sum of squares of resists of each model. On the other hand, the optimal conformable fractional order and dynamic background value coefficient are determined by the quantum inspired evolutionary algorithm (QIEA). Taking the resident population from 2008 to 2018 as the research object, the optimal weighted combination model and fractional grey model were used to study the estimated and predicted values. The results are compared and analyzed. The results show that the fractional grey model is better than the optimal weighted combination model in the estimation of the values. The optimal weighted combination model is better than the fractional grey model in predicting. Meanwhile, the fractional grey model is found to be very suitable for the data values that are large, and the changes between the data are relatively small. The research results expand the application of the fractional grey model and have important implications for the policy implementation activities of Huizhou government according to the population growth trend in Huizhou.
Highlights
With the massive urbanization of the population, population development is still an important issue in China. e current situation of population development is that the proportion of the population with young children is gradually decreasing, and the proportion of the elderly is gradually increasing [1]
This paper develops the research and obtains good results. e results of the fractional grey model and the optimal weighted combination model are compared and analyzed
FGM (1, 1) Mode. e fractional grey model is firstly propounded by Wu et al [15]; the results showed that compared with the traditional grey model, this model greatly improves the prediction accuracy
Summary
With the massive urbanization of the population, population development is still an important issue in China. e current situation of population development is that the proportion of the population with young children is gradually decreasing, and the proportion of the elderly is gradually increasing [1]. E other is the population prediction method, which uses birth, death, and migration data to explore population trends. The cohort factor method sets parameters such as death, birth, and migration and structural change according to the input parameters of population size. In the short term forecast, Professor Deng [7] established a grey system to be used in population forecasting He regarded the population system as a grey system based on model uncertainty and model mathematics, which could obtain more accurate prediction results without too much basic data. Chen [8] established the progressive population development model of children’s subclass age and made a new attempt to predict population based on demographic structure factors.
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