Research on Planting Strategies Based on Improved Multi-Objective Genetic Algorithm and Monte Carlo Simulation

NSGA-II is a multi-objective optimization algorithm based on genetic algorithm in the field of machine learning, which is mainly used to deal with complex optimization problems with two or more objective functions. The NSGA-II algorithm performs well in solving multi-objective optimization problems, and is widely used in engineering design optimization, scheduling optimization, resource allocation optimization and other fields, which can seek balance between multiple objectives and provide valuable Pareto optimal solution sets for decision makers. In order to expand the application scope of the NSGA-II algorithm, this research applied it to the optimization of crop planting strategies. Specifically, by analyzing the yield per mu, planting cost and sales price of crops, a single-objective linear programming model was established, and the improved genetic algorithm was used to solve the planting scheme to maximize economic benefits in the next seven years. To further consider the uncertainty of market fluctuations, Monte Carlo simulation is introduced in this research, and a dual-objective robust optimization model including sales, selling price, and cost changes is constructed, and the improved NSGA-II algorithm is used to solve the optimization. This not only obtains the optimal planting scheme considering the risk factors, but also provides a new solution for solving similar multi-objective optimization problems.