基于改进灰狼优化算法的边缘计算任务调度方法

针对边缘计算在离散制造业数据处理过程中存在的时延和资源消耗大的问题，提出了一种基于改进灰狼优化(IGWO)算法的边缘计算任务调度方法。该方法通过对非线性收敛因子以及动态权重的改进，提高了灰狼算法的优化速度和精度，有效降低了终端设备和边缘端的资源损耗以及任务处理的时延。基于不同数据任务量下的处理时延与资源消耗实验，证明了所提模型的有效性，与3种主流任务调度算法相比，数据处理资源消耗和时延最低。将边缘计算任务调度与智能寻优算法相结合并运用到离散制造业，可以提高设备任务的处理速度、降低能耗，为离散制造业智能化转型提供借鉴。

Edge Computing Task Scheduling Method Based on an Improved Grey Wolf Optimization Algorithm

To mitigate time delay and high resource consumption of edge computing in discrete manufacturing data processing, a task scheduling method for edge computing based on an improved gray wolf algorithm is proposed. By improving the nonlinear convergence factor and the dynamic weight, the optimization speed and accuracy of the grey wolf algorithm are improved, and the terminal and edge terminal resource loss and the delay of task processing are effectively reduced. Experiments on processing delay and resource consumption under different data tasks prove the effectiveness of the proposed model. Compared with the three main task scheduling algorithms, the proposed model achieves the lowest data processing resource consumption and delay. Furthermore, by combining edge computing task scheduling with an intelligent optimization algorithm and applying it to discrete manufacturing, the task processing speed of equipment is improved, and the energy consumption is reduced. This model serves as a reference for the intelligent transformation of discrete manufacturing.