Optimization Research on Rural Crop Planting Strategies Based on Greedy Algorithm and PSO

Abstract

As the crop market continues to expand and national policies support sustainable rural development, it is increasingly important to safeguard farmers' interests and crop cultivation. In order to help a rural village develop a planting strategy for the next 7 years according to the local conditions, this paper preprocesses the crop production and planting area based on the data of a village in 2023, and calculates and visualizes its future sales volume. After that, a single-objective optimization model is established to plan the planting strategy for the next 7 years, considering two scenarios: over-selling is regarded as stagnant selling or selling at 50% price reduction of the original price, and setting up the corresponding objective function and constraints. In this paper, the greedy algorithm is firstly used to solve the problem step by step, based on the optimal solution of the previous year to obtain the optimal solution of the next year as much as possible, aiming to maximize the profit of the next seven years. However, the analysis and discussion show that the greedy algorithm is only a local optimum. Therefore, this paper further adopts the particle swarm algorithm to optimize the model and re-solve to obtain a better solution as the final planting strategy.