An Improved Grey Wolf Optimizer for Task Scheduling in D2D-Assisted MEC Systems

The rapid development of 5 G and Internet of Things (IoT) has led to a surge in connected wireless devices, creating a significant demand for computation due to the evolution of intelligent applications. Addressing the computational pressure on Mobile Edge Computing (MEC) is now an urgent concern. Effectively offloading intensive tasks via Device-to-Device (D2D) links to the base station (BS) or idle devices improves computation quality and reduces latency. In this paper, we propose a D2D-assisted MEC system to address the scheduling challenges posed by numerous independent computing tasks generated by multiple users. We consider splitting the user’s task into multiple independent subtasks and calculating offloading separately to reduce processing delay. We represent this scheduling problem in the form of a task permutation and propose an improved Grey Wolf Optimizer (IGWO) metaheuristic algorithm to search for the optimal scheduling solution. This approach, through improvements to the nonlinear convergence factor and dynamic weighting, enhances the optimization speed and accuracy of the Grey Wolf algorithm, effectively reducing task processing latency. Simulation results indicate that the IGWO metaheuristic algorithm outperforms other benchmark methods in addressing this scheduling problem.