Research on Optimisation Strategies Based on Particle Swarm Algorithm

In this paper, based on the crop cultivation data of a village in the mountainous region of North China during the period from 2024 to 2030, a research on the optimisation of cultivation strategies was conducted, aiming to cope with climate change, cultivation risks, market fluctuations, and resource constraints, and to solve the problem through the use of multi-stage optimisation strategies, the establishment of a multiple linear regression model, and the use of particle swarms. Firstly, a multi-stage optimisation model was established, the essence of which is to make the best decision for the next year's planting based on the previous year's data in order to maximise the profit in each year thereby maximising the total profit from 2024 to 2030. Considering the selectivity of different crops for different arable lands, we simplified the crop types and determined the final mathematical model for food crops and non-food crops under two scenarios of stagnant sales and selling at reduced prices. The simplification of crop types lays the foundation for the solution of subsequent models. In addition, considering the changes in planting costs and mu yield under climate change and market fluctuations, which affect parameters with instability, we established an uncertaintybased stage optimisation model based on the particle swarm algorithm. The model can dynamically adjust the planting programme to cope with the uncertainty of market and climate so that it can maintain good profitability under different environments.