Research on Crop Planting Strategies Based on Multi-Objective NSGA-III Optimization Algorithm and Robust Optimization

This paper conducts an indepth analysis of rural crop planting strategies based on crop planting data, aiming to optimize the allocation of planting areas and crop selection. First, the data is meticulously preprocessed. Subsequently, multi-objective optimization models are constructed separately for potential stagnant sales and price reduction scenarios. These models have two optimization objectives: maximizing planting efficiency and maximizing planting concentration. During the model construction, various constraints such as planting area, crop yield, selling price, and plot planting conditions are comprehensively considered. Given the complexity of these constraints, to enhance computational efficiency, the NSGA-III algorithm is selected to solve the models. Furthermore, to address potential future uncertainties in variables such as sales volume, yield per unit area, and selling price fluctuations, an interval variation model is established. This model aims to better adapt the existing models to future changes in planting strategies. To enhance the robustness of the models, robust optimization methods are introduced in this paper. These methods ensure that the models perform well even under extreme conditions, thereby guaranteeing the stability and sustainability of planting strategies. Finally, the particle swarm optimization algorithm is used to solve the models, predicting and evaluating planting efficiency under future uncertainty conditions.