Particle Swarm Optimization for Agricultural Problems

Abstract

The research objective of this paper is to provide guidance for crop planting planning, thereby assisting farmers in maximizing their profits. In the development of rural economies, efficiently utilizing limited arable land resources poses a significant challenge. To simulate real-world scenarios, the paper first introduces the "dichotomy model" of bargaining between sellers to calculate the actual transaction amount. Next, it establishes constraints based on the requirements of arable land and crops. Finally, using the Particle Swarm Optimization (PSO) algorithm, it predicts the optimal crop planting strategies for the period 2024-2030 under different decision-making scenarios. The first decision-making scenario yields a maximum profit of 3.65 million yuan, while the second scenario results in a maximum profit of 7.77 million yuan. The experimental research concludes that selling surplus products at discounted prices can maximize profits, and it also demonstrates that this model can accurately predict future sales performance.