Analysis and Re-Optimizing Food Systems by Using Intelligent Optimization Algorithms and Machine Learning

In the context of serious challenges to global food security, this research focuses on the feasibility of improvements to food systems and predicting future changes. This paper establishes a coordination evaluation index on diverse indicators of different food systems, analyzes the changes after modification, and predicts the analysis of equilibrium time and critical point. In this regard, we pre-processed the data by entropy method, variance contribution rate, and normalization. Instead of single-element linear forecasting and economic income and expenditure models, we innovatively developed a multivariate evaluation system for population, cultivated land, and food systems in three major directions. Meanwhile, after conducting a cross-sectional comparison of the prediction effects of various algorithms, we finally selected Gaussian Process Regression and Neural Network to establish a prediction model for the development of food systems of different sizes. After establishing the evaluation index and the development prediction model, we fit the three-dimensional surface of the development change by using the thin-plate interpolation. We adopt the swarm intelligence optimization algorithm to search for the balance and critical points after the change. We also compared various swarm intelligence optimization algorithms, such as particle Swarm Optimization Algorithm, Salp Swarm Algorithm, and Whale Optimization Algorithm.