Research on the Optimization of Crop Cultivation Strategies Based on Improved NSGA-II Algorithm and Differential Evolution Algorithm

This study utilizes an improved NSGA-II and Differential Evolution Algorithm to optimize crop cultivation strategies. Analyzing data from various rural plots in 2023, the research establishes a bi-objective linear programming model aimed at maximizing the total sales profit for crop planting from 2024 to 2030. The model accounts for uncertainties in sales volume, yield, costs, and prices, employing orthogonal experimental design to assess multiple factors efficiently. The NSGA-II algorithm is applied to find optimal solutions that balance the dual objectives of profit maximization. The study also incorporates parameters like substitution rates and land ratios to analyze correlations between sales, costs, and prices, enhancing the model's realism. A multivariate nonlinear regression model, optimized by the Differential Evolution Algorithm, quantifies the relationships among these factors, aligning the expected sales volume with pricing and cost dynamics.