移动边缘计算中基于混合人工蜂群算法的计算卸载策略

Computational offloading strategy based on hybrid artificial bee colony algorithm in mobile edge computing

计算卸载是移动边缘计算(Mobile Edge Computing,MEC)中的关键技术.针对多用户多MEC服务器场景中计算卸载策略的不足,本文提出一种混合人工蜂群算法(Artificial Reverse Sine-Cosine,ARSC).首先,使用反向学习策略初始化种群,优化种群的初始解;然后,在雇佣蜂阶段利用正余弦算法的全局最优引导信息,提升算法的局部搜索能力;最后,为了平衡算法的全局搜索能力和局部搜索能力,引入动态感知因子对算法的步长因子进行改进.仿真实验结果表明,相比基于粒子群算法的卸载策略、基于人工蜂群算法的卸载策略,ARSC策略在系统时延、系统能耗、收敛性等指标上均有所改善.

Computational offloading is an essential technology in Mobile Edge Computing(MEC).To address the shortage of computational offloading strategies in multi-user and multi-MEC server scenarios,this paper proposes a hybrid artificial bee colony approach(Artificial Reverse Sine-Cosine,ARSC).First,the opposition-based learning strategy is used to initialize the population and optimize the initial solution of the population.Then the global optimal bootstrap information of the sine-cosine algorithm is exploited to improve the local search capability in the employed bee stage.Finally,to balance the global and local search capability of the approach,the step size factor is adapted by introducing dynamic perception.Simulation results show that the proposed ARSC approach outperforms offloading strategies based on particle swarm algorithm and artificial bee colony algorithm in convergence,latency,and energy consumption.