基于MNC-PSO的循环农业耦合资源智能分配方法

Intelligent Location Method of Coupling Resources in Circular Agriculture Based on Multivariate and Nonlinear Constraint Particle Swarm Optimization

智慧农业为生态农业发展提供了有效的方法论。为了实现生态农业资源的有效分配,根据优化模型特点,提出了一种基于多元变量、非线性约束粒子群(MNC-PSO)算法来实现循环农业资源的智能规划。通过分析构建循环农业系统模型,界定模型的系统边界,创新地将生态效益通过氮平衡转化为约束条件,再依据经济效益建立目标函数,构建了多元变量、非线性约束的多目标粒子群算法。以"猪-玉米"循环农业生产作为研究对象,结果表明:当种植规模为9.85~13.64 hm^(2),养殖规模为725~931头,达到了经济效益、生态效益与耦合资源的平衡优化。

Smart agriculture is a crucial methodology for ecological agriculture. To optimize allocation of circular agricultural resources in ecological agriculture, according to the characteristics of the optimization model, coupling location method based on Multivariate and Nonlinear Constraint Particle Swarm Optimization(MNC-PSO)was proposed. Firstly, the model of circular agriculture system was analyzed and constructed, the system boundary of the model was defined, and the ecological benefits were transformed into constraints through nitrogen balance innovatively. Then the objective function was established according to the economic benefits, and the multi-objective particle swarm optimization algorithm with multivariate variables and nonlinear constraints was constructed. Taking "pig-corn" circular agriculture as an example, the experimental results show that when the planting scale is 9.85-13.64 hm^(2) and the breeding scale is 725-931 heads, the balance optimization of economic benefits, ecological benefits and coupling resources is achieved.