Computational offloading strategy based on hybrid artificial bee colony algorithm in mobile edge computing
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.
mobile edge computing(MEC)computation offloadingartificial bee colony algorithmsine-cosine al-gorithmmulti-user and multi-MEC server
万方数据
维普
