Research on optimization methods for solving food crisis based on factor analysis and time prediction

Nowadays, millions of people are facing food crisis, which has a certain thing to do with the current food system structure of various countries. In order to optimize current food system, this paper collects 18 food security indicators from five developed countries and developing countries, determines the weights of various indicators using the entropy weight method, and establishes a Multi - level evaluation model based on the analyzed indicators. This paper uses a Multi - objective fuzzy optimization model to optimize the priority of fairness and sustainability in food systems, redefines the weights, and analyzes the changes and effects of the parameters of the optimization system. Principal component analysis method was used in this paper to further screen the optimized food system parameters and determined the priority of the factors affecting the food system and make sure that the optimized food system effectively improves the existing fairness and sustainability problem. Finally, this paper uses the PCA-PSO-BP neural network prediction model to predict the optimized food system, and predicts that the new system can reach our expected level around 2042.