Analysis of Crop Planting Strategies Based on Genetic Algorithm and Simulated Annealing Algorithm

This paper is based on genetic algorithm and simulated annealing algorithm aims to cope with the optimization of crop planting in a rural village in the mountainous area. In the paper, matrix idea and non-convex optimization theory are used to construct a single-objective planning model and solve the problem by combining genetic algorithm and simulated annealing algorithm. In the face of different market conditions, different strategies are adopted: under the stable market, the over-yield treatment strategy is explored to optimize the planting plan; in the dynamic environment, the impact of variable fluctuations on the planting decision is taken into account with the help of uncertainty modeling and robust optimization methods; furthermore, crop substitutability, complementarities, and market correlation are also taken into account, and the objective function is optimized through the analysis of Spearman's correlation coefficient and K - Means clustering. The objective function was optimized by Spearman correlation and K-Means clustering analysis. The research in this paper provides a comprehensive and effective strategy to cope with planting under low temperature limitation, which is of great significance to the development of rural planting.