A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction

In any economy, it is essential to monitor the rate of population change closely. Governments employ various strategies and programs to regulate population growth since different population growth rates have distinct economic consequences. This paper reveals a global trend of reduced desire to have children, with variations across countries. The paper aims to predict the population growth rate in England by employing Artificial Neural Networks (ANN) in combination with various meta-heuristic algorithms, including the Sparrow Search Algorithm (SSA). The selection of SSA and other algorithms is based on factors such as accuracy and computational efficiency. A set of 18 economic indicators serves as input variables, and a Genetic Algorithm (GA) is used for feature selection. The data used for analysis spans the most recent ten years and is presented on a monthly basis. The results indicate that SSA exhibits the lowest prediction errors for the population growth rate among the applied algorithms in this paper. The primary contribution of this study lies in the application of hybrid algorithms that combine SSA-ANN with other algorithms, such as LA. The paper also emphasizes the inclusion of influential and impactful indices as input variables to enhance prediction accuracy.