关键词: 移动边缘计算, h-SOM神经网络, 改进差分进化（IDE）算法, 双变异策略

Abstract:

Mobile Edge Computing（MEC） is an application extension of cloud computing technology on top of edge infrastructure. An energy-efficient UAV mobile edge computing system is considered to minimize system energy consumption by jointly optimizing the UAV’s motion trajectory, computation offloading, and resource allocation. In order to solve the above problems, a two-layer optimization method is proposed, which uses real-time clustering of users based on unsupervised learning based channel gain-Self-Organizing Feature Map（h-SOM）, which is based on channel gain as an indicator of the judgment category and get the optimal deployment position of the drone. in the lower layer, according to the deployment of the UAV, the computation offloading and resource allocation problem is converted into a Mixed Integer Nonlinear Programming Problem（MINLP）, and the double-variation Improved Differential Evolution（IDE） algorithm with elite initial strategy and adaptive operator is adopted. The elite initial strategy can improve the accuracy of the algorithm, the double mutation method can improve the global search ability of the algorithm and promote the convergence of the algorithm, thus obtaining better task unloading and resource allocation decision. The effectiveness of the proposed method is verified by simulation experiments, and compared with the traditional algorithm, the optimization effect is significant, which provides a new idea for the joint optimization of MEC system.

Key words: mobile edge computing, h-SOM neural network, Improved Differential Evolution（IDE） algorithm, double mutation strategy