Urban population prediction based on multi-objective lioness optimization algorithm and system dynamics model

Abstract

Population size is closely related to economic and social development and change. It is one of the primary and essential elements of overall urban development planning to formulate a population development strategy scientifically through population projections. Therefore, we propose an urban population prediction model based on a multi-objective lioness optimization algorithm and system dynamics. The multi-objective lioness optimization algorithm is used to optimize some critical parameters of the system dynamics model to reduce the subjectivity of the model construction. Taking Xi’an as an example, the validity of the model is verified, and the population size of Xi’an from 2019 to 2050 is predicted by the model. In addition, the impact of different policies and their combinations on the future population is discussed through simulations of three scenarios composed of five policy factors: birth, employment, science and technology, healthcare and education. The results show that the total population of Xi’an will peak at 147,939,242 in 2040, based on current development trends. Moreover, the five policies with the largest to smallest positive effect on population size are: employment policy, fertility policy, education policy, science and technology policy, and health policy, with employment and fertility policies having significantly larger effects than the other three. Therefore, the employment policy and the birth policy are the two most effective policies to promote population growth, and the coordinated implementation of the five policies is the fastest way to increase population size.