Optimal Planting Strategy of Crops Based on Particle Swarm Optimization Algorithm

Abstract

Food security is the top priority related to the national economy and people's livelihood. In the context of the shortage of cultivated land resources, rational planning of cultivated land resources planting is an important measure to improve the utilization rate of cultivated land. This study focuses on the use of improved particle swarm optimization to improve cultivated land use efficiency. In order to solve the shortcomings of the traditional particle swarm optimization in dealing with cultivated land use problems, such as slow convergence speed and easy to fall into local optimum, improved strategies such as adaptive inertia weighting and variation operation were introduced. By constructing an optimization model with cultivated land output benefit and resource utilization as the objective function, the improved algorithm is applied to the actual cultivated land use scenario for simulation experiments. Experimental results show that the improved algorithm can quickly converge to the optimal solution, significantly improve the rationality of spatial allocation and utilization efficiency of cultivated land, provide an effective technical means for the efficient management and rational planning of cultivated land resources, and have important theoretical and practical significance for promoting the sustainable development of agriculture.