Mobile Edge Computing Offloading Problem Based on Improved Grey Wolf Optimizer

Mobile edge computing technology, the goal is to reduce the computational pressure of terminal devices, improve the utilization of computational resources.For the optimization problem of mobile edge computing network with wireless transmission, firstly, an improved grey wolf optimizer algorithm (OPGWO) for task scheduling is proposed, and the initial population is generated by using Latin hypercube sampling in the population initialization phase, and the orthogonal inverse strategy is introduced in the optimization seeking phase, and the effectiveness of the OPGWO is verified on the CEC 2017 test function. Then a resource allocation method of V-function mapping policy is proposed, and the edge computing model is simulated by simulation experiments under different task requests, and the joint optimization scheme proposed in this paper is compared with local offloading policy, random offloading policy, Genetic Algorithm (GA) and Deep Q network algorithm (DQN), which has the best performance in terms of performance and total energy consumption of the optimized system and the best optimization effect.