Computing Offloading Decision Based on Adaptive Estimation of Distribution Algorithm in Internet of Vehicles

Aimed to improve the efficiency of computing offloading in internet of vehicles (IoV), a collaborative multi-task computing offloading decision mechanism with adaptive estimation of distribution algorithm for MEC-IoV was proposed in this paper. The algorithm considered the energy and time consumption as well as priority among different tasks. It presented a local search strategy and an adaptive learning rate according to the characteristics of the problem to improve the estimation of distribution algorithm. Experimental results show that compared with other offloading strategies, the proposed offloading strategy has obvious effects on the total cost optimization; the solutions quality of AEDA is 86.6% of PSO and 67.3% of GA.