Optimization Research On Planting Pattern Based On Virtual Model

In recent years,with the rapid growth of population and the reduction of arable land area,how to improve crop yields in agricultural production has become a hot research topic.Regarding the improvement of crop yield,the existing methods are mainly to change the planting spacing,or to intercropping or rotate with other varieties of crops.Changing planting patterns is an important method to increase crop yield without changing the varieties of plants.However,in the agricultural production of real life scene,if we wanted to optimize the planting pattern,it would consume a lot of resources,such as manpower,material resources and financial resources,and there is no guarantee that the experimental environment in each experiment can be same.Therefore,in order to obtain the optimal planting pattern of the crop,we could the combining the virtual model with optimization algorithm to optimized the planting pattern.Based on different crop functional structure models,the different optimization algorithms are applied to the planting patterns within different crops,so that the flora which will be optimized can has accurate evaluated in the experimental environment.The main work and results of this paper are as follows:1.An improved particle swarm optimization algorithm was proposed.Particle swarm optimization(PSO)is one of the most popular classical group intelligent optimization algorithms.On the basis of PSO,this paper analyzes the problems of the local extremes and the convergence speed,the mined PSO combined the different update formulas to balance the exploration ability and convergence ability of the PSO algorithm.Through the multi-level disturbance to improve the shortcomings of the local extremes,a mixed PSO algorithm with multistage disturbances is proposed to improve its optimization ability and convergence ability,which greatly improves its optimization ability and convergence ability compared with the two improved particle swarm optimization algorithms.2.A shelter algorithm was proposed.According to the characteristics of the planting model to be optimized,it was found that the problem was related to the dimension by analyzing the characteristics of the problem.Whether it is an independent variable or a dependent variable,their optimal values will vary with the dimensions.At the same time,for the local extremes problem of the group intelligent optimization algorithm,we consider the local extremes as the factor that can influence the update direction in the optimization algorithm.Therefore,a new shelter optimization algorithm is proposed,which uses the local extremes obtained in the optimization process as a shelter,communicates between different shelters and explorers in the shelter.The extremes are factors that can influence the moving path of the explorer.At the same time,the experimental results show that the algorithm can solve the problem related to the dimension well,and also shows better than the mixed particle swarm optimization algorithm and the improved gray wolf optimization.3.A general combination flow based on virtual model and optimization algorithm is proposed.Firstly,the characteristics of the virtual model were analyzed,and it was found that it can accurately obtain various indicators that are difficult to obtain in real life.Secondly,the optimization algorithm can approximate the optimal solution position of the evaluation function composed of the obtained indicators.Therefore,the information of the planting mode is given to the virtual model through the algorithm agent point,and after the virtual model grows,various indicators are obtained to feed back the algorithm and the agent point to search for the optimal planting mode.The research on crop planting pattern optimization based on virtual model studied in this paper combines different algorithms into different virtual model communities through a versatile combined process to obtain the optimal planting patterns of different communities.Two novel optimization algorithms are well suited for this research task.In the real agricultural production and life,the direction of production has been improved.