Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm - Yuliang CONG; Wenxi SUN; Ke XUE; Zhihong QIAN; Mianshu CHEN (Tongxin xuebao)
Aiming at the problem of unreasonable resource allocation caused by the unloading decision in the multi-vehicle and multi-server IoV scenario, a two-stage heuristic IoV task offloading strategy was proposed.This strategy used the improved hybrid genetic algorithm (IHGA) and the improved artificial fish swarm algorithm (AFSA), combined with the system’s internal average overhead, delay and energy consumption requirements, the two improved algorithm was used for multiple iterations to achieve optimal resource allocation in the process of task unloading.The simulation results show that the proposed scheme can effectively reduce the system internal overhead and improve the task offloading efficiency compared with the benchmark scheme.