Crop Planting Optimization Based on Monte Carlo and Multiple Genetic Algorithms

In this study, an optimization model based on Monte Carlo method and multiple genetic algorithms is proposed with the aim of optimizing the crop planting scheme. Based on the known economic parameters of the crops, a bi-objective model with the objectives of maximizing the total profit and maximizing the entropy degree of field management was firstly established. The efficiency of field management was assessed by the entropy degree and the model was optimized by combining the inverse generalization coefficient. Secondly, the Monte Carlo method was used to randomly generate a sample set of parameter fluctuations, find the maximum risk parameter through robust planning, and optimize the economic efficiency and entropy maximization model on this basis. Further, the substitutability and complementarity among crops are quantified by covariance analysis and a clustering model of crop substitutability is established, so as to effectively adjust the planting combination of crops.