Crop Optimization Model Based on Robust Genetic Algorithm

Abstract

In the context of the current rural economic transformation, the optimization of crop planting structure has emerged as a critical task, particularly in light of limited resources, market fluctuations, and environmental challenges. This paper addresses the practical issues of constrained rural cultivated land resources and multiple restrictions on crop cultivation in the mountainous regions of North China by constructing a hybrid model that integrates robust optimization and interval planning. The model accounts for the interval fluctuations of future expected sales, yield per unit area, planting costs, and selling prices, and employs a genetic algorithm to simulate the natural evolution process, thereby seeking an approximate optimal solution. By proposing an optimal crop planting plan for the period from 2024 to 2030, this study not only provides academic support but also offers practical guidance for navigating a complex and ever-changing agricultural environment. The findings aim to enhance the resilience and sustainability of rural economies, ensuring that they can adapt to both current and future challenges in agricultural production and resource management.