Task Offloading and Resource Allocation based on Enhanced Artificial Hummingbird Algorithm in NOMA-Assisted Mobile Edge Computing

Mobile edge computing (MEC) is a key technology of the Internet of Vehicles (IoV). Applying non-orthogonal multiple access (NOMA) technology to MEC can improve spectrum and avoid serious computing delay. This paper studies the optimization problem of task offloading and resource allocation under the NOMA-MEC system. By jointly optimizing the task partition ratio, transmit power allocation and computing resource allocation on the MEC server, the goal is to minimize the weighted total delay of the system. An enhanced artificial hummingbird algorithm (EAHA) is proposed to solve the optimization problem. Considering the task priority weights of each vehicle user, the SIC decoding mechanism of the NOMA technology is improved, and the chaos map, Levy flight strategy and whale algorithm are added to improve the population initialization and migration strategy in the AHA algorithm. The problem of uneven population distribution and repeated individuals in the original algorithm is solved, and the limitation of the algorithm that it is easy to fall into the local optimal solution is improved. Finally, the optimization solution of task offloading and resource allocation problems is realized, so that vehicle users can obtain the optimal offloading strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the total delay of the system and has considerable convergence speed and accuracy.