An Improved Binary Grey Wolf Optimizer for Dependent Task Scheduling in Edge Computing

As an emerging computing paradigm, edge computing has attracted lots of interest from both academia and industry recently. By offloading tasks to the devices at the edge of the network, edge computing reduces service delay and bandwidth consumption. So the task scheduling algorithm influences the capabilities of edge computing significantly. In this paper, an improved binary grey wolf optimizer (IBGWO) is proposed to solve the dependent task scheduling problem in edge computing. By upgrading the convergence parameter and the binarization transfer function, IBGWO obtains rapid convergence, stable approximate optimal solutions and a proper balance between exploration and exploitation. Simulation results demonstrate that the proposed IBGWO outperforms binary bat algorithm (BBA) and binary particle swarm optimization (BPSO) in convergence, accuracy, and stability.