Research on Planting Planning Strategies Based on Optimization Genetic Algorithms under Multiple Constraints

This paper presents a study on planting planning strategies based on optimization genetic algorithms under multiple constraints. The research focuses on establishing a linear programming model with the decision variable of the acreage of crop p planted in seasons of year y on plot l, with the maximization of yield as the optimization objective. An optimized genetic algorithm is designed for solving this model. The optimization method introduces a multi-matrix encoding scheme tailored to enhance the decoding efficiency of chromosomes specific to the problem studied in this paper. Additionally, a repair scheme for infeasible solutions is devised to address the multi-constraint nature of the problem. Finally, two surplus disposal scenarios are considered for the solution, demonstrating that the designed optimization genetic algorithm effectively solves the model. The results show that the algorithm progressively converges to the optimal solution during iterations, reaching a stable state after a relatively low number of iterations.