Research on Planting Strategy Based on Game Theory Hybrid Particle Model

Abstract

Hybrid particle swarm optimization (PSS) is an intelligent optimization method widely used in machine learning. In this paper, we combine evolutionary game theory and particle swarm optimization and apply them to the optimization of farm planting strategies. Firstly, a linear regression model was established by accurately modeling the output, sales volume and other relevant indicators of agricultural products. Then, by using the powerful global search ability of game theory and hybrid particle swarm optimization to solve the optimal solution under constraints. Subsequently, the Monte Carlo method is introduced to incorporate factors such as climate and geographic location into the model, which further improves the accuracy of the prediction. This study not only successfully applies game theory and hybrid particle swarm optimization to the optimization of farm planting strategy, which has high practical value, but also provides a useful reference for similar multi-dimensional programming problems.