Study on Crop Planting Strategies Based on Multi-Objective Optimization and Uncertainty Management

According to the current agricultural research in the mountainous areas of North China, there are significant deficiencies in coping with uncertain factors, especially planting planning strategies under the dual impact of climate change and market fluctuations. In this paper, the traditional linear programming model is improved, and an innovative multi-objective optimization model is proposed, which combines efficient algorithm application and uncertainty processing. Firstly, Gurobi optimizer is used to deal with the Optimization problem accurately, and then the method of combining Latin Hypercube Sampling (LHS) and Particle Swarm Optimization (PSO) is adopted. To deal with uncertainty in multidimensional parameter space. LHS is used to generate uniformly distributed initial samples to improve the search efficiency of PSO. At the same time, a rapid precursor elimination technique based on probabilistic distance is combined to further optimize the solution selection process. Finally, we use the grey Wolf optimization algorithm to solve the multi-objective programming model and get the Pareto optimal solution set. In this method, the optimal planting scheme is formulated by considering the uncertainty factors. The improved model can incorporate many uncertainties and crop correlations, and project planting plans for the next few years with the goal of maximizing returns. The innovation of the model is to put forward a multi-stage optimization framework to provide new ideas and practical solutions for agricultural fine management and sustainable development.