Optimization of Crop Planting Based on Genetic Algorithm and Monte Carlo Simulation

This study focuses on the optimization analysis of crop cultivation in mountainous villages using genetic algorithm and Monte Carlo simulation, aiming to formulate the optimal crop cultivation scenarios for the period of 2024–2030 through mathematical modeling and optimization algorithms. The study first considered two possible scenarios (stagnant sales and price reductions) and developed a genetic algorithm-based total return maximization model to solve the optimal planting scheme for different sales scenarios in the next five years. Next, the uncertainty of various economic factors is analyzed under an uncertainty environment, and the Monte Carlo simulation algorithm is used to solve the optimal planting scheme. Finally, the optimal planting plan is further optimized by linear programming methods, considering the complementarity and substitutability between crops. The research in this paper not only provides a feasible optimization path for agricultural cultivation in a specific region, but also provides a valuable solution to cope with the uncertainty and risk in agricultural production.