改进灰狼算法在土壤墒情监测预测系统中的应用

针对现有的固定端传感器土壤墒情监测预测系统架设成本高、传感器易损坏、预测精度较低等问题，设计并实现了基于非固定无线传感器组网与改进灰狼算法优化神经网络的土壤墒情监测预测系统。系统使用非固定即插即用式传感器蓝牙组网收集墒情数据，使用高精度多源定位接入融合方法进行广域室外高精度定位。在算法方面，针对灰狼算法在迭代中后期易陷入局部最优等问题，提出一种基于末尾探索者策略的改进灰狼算法。首先，根据种群个体适应度值排名，在原有算法个体类型中增加探索者类型。然后，将种群搜索分为三个时期：活跃探索期、周期探索期和种群回归期。最后，在每个时期使用特有的位置更新策略进行探索者位置调整，使得算法在探索初期更具随机性，在探索中后期依然保持一定的解空间搜索能力，从而增强算法的局部最优回避能力。使用标准函数进行算法性能测试，并将该算法应用于优化土壤墒情神经网络预测模型问题，使用某市2号试验田的数据进行实验。实验结果表明，所提算法与直接神经网络预测模型相比，相对误差下降约4个百分点；与传统灰狼算法、粒子群优化（PSO）算法优化模型比较，相对误差下降约1至2个百分点。所提算法拥有更小的误差，更好的局部最优回避能力，能有效提高墒情的预测质量。

Application of improved grey wolf optimizer algorithm in soil moisture monitoring and forecasting system

Focusing on the issues of high cost, high susceptibility to damage and low prediction accuracy of soil moisture monitoring and forecasting system, the soil moisture monitoring based on non-fixed wireless sensor network and improved grey wolf algorithm optimization neural network was designed and implemented. In the proposed soil moisture monitoring system, non-fixed and plug-in sensor bluetooth network was used to collect moisture data, and high-precision multi-source location access fusion method was used for wide-area outdoor high-precision positioning. In terms of algorithms, focusing on the issue that Grey Wolf Optimizer (GWO) algorithm easily falls into local optima in its later iterations, an improved GWO algorithm based on rearward explorer mechanism was proposed. Firstly, according to the fitness value of the population, the explorer type was added to the original individual types of the algorithm. Secondly, the search period of population was divided into three parts: active exploration period, cycle exploration period and population regression period. Finally, the unique location updating strategy was used for the explorer during the different period, which made the algorithm more random in the early stage and keep updating in the middle and late stages, thus strengthening the local optimal avoidance ability of the algorithm. The algorithm was tested on the standard functions and applied to optimize the neural network prediction model of soil moisture system. Based on the datasets obtained from the experimental plot No. 2 in a city, the experimental results show that the relative error decreases by about 4 percentage points compared with the direct neural network prediction model, and decreases by about 1 to 2 percentage points compared with the traditional GWO algorithm and Particle Swarm Optimization (PSO). The proposed algorithm has smaller error, better local optimal avoidance ability, and improves the prediction quality of soil moisture.