Crop Planting Optimization Based on Simulated Annealing Algorithm and Genetic Algorithm

Based on simulated annealing algorithm and genetic algorithm, this study aims to provide an effective planting strategy to improve the efficiency of crop production in a village in the mountainous area. Firstly, a multi-objective optimization model is established for the agricultural production characteristics of this countryside, and the Nondominated Sorting Genetic Algorithm II (NSGA-II) is used for global search and combined with the Simulated Annealing Algorithm for local optimization. Secondly, in order to cope with various uncertainties, this paper introduces genetic algorithm (GA) to simulate the biological evolution process and combines with particle swarm optimization algorithm (PSO) to accelerate the convergence and seek for the planting combination that maximizes the profit. Finally, considering the substitutability and complementarity of crops, the study designed a set of comprehensive optimization models, using Spearman's correlation analysis and Kendall's tau-b correlation analysis, to establish a multi-scenario optimization strategy to adapt to market demand and climate change.