Research on Planting Optimization Strategies Based on Intelligent Algorithms

To address the dynamic optimization challenges in multi-cycle agricultural planting planning, where traditional linear programming exhibits inadequate adaptability to non-linear characteristics and single-objective optimization fails to effectively balance the trade-off between economic benefits and yield risks, this study proposes a hybrid algorithm integrating Monte Carlo simulation with an enhanced NSGA-II approach. First, we establish a bi-objective planning model maximizing economic benefits while minimizing yield fluctuations, incorporating Land Equivalent Ratio constraints (LER ≥ 1.4). Subsequently, a dynamic parameter adjustment mechanism is designed, employing the PuLP solver to handle complex constraints. Validation through 2024-2030 planting data from mountainous villages in North China demonstrates: (1) 18.7% revenue enhancement compared to traditional NSGA-II solutions ($46, 919, 181 to $55, 326, 752); (2) 23.5% reduction in yield fluctuations.(3) 37.8% improvement in computational efficiency, especially in land allocation and crop rotation decisions.This research provides a novel methodology for automated agricultural decision-making,offering practical value for optimizing planting strategies in dynamic environments.