基于改进的混合遗传算法的车联网任务卸载策略研究

Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm

针对多车多服务器车联网场景下的卸载决策造成资源分配不合理的问题,提出了一种两阶段启发式的车联网任务卸载策略。该策略利用改进的混合遗传算法(IGHA)与改进的人工鱼群算法(AFSA),结合系统对其内部的平均开销、时延与能耗的要求,对2种改进算法进行多次迭代求解,在任务卸载过程中实现最优资源分配。仿真实验结果表明,相比于基准方案,所提方案可以有效地降低系统内开销并提高任务卸载效率。

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.