Task Offloading Strategy of Vehicular Networks Based on Improved Bald Eagle Search Optimization Algorithm

To reduce computing delay and energy consumption in the Vehicular networks, total cost of task offloading, namely consumption, is studied. A offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server cloud proposed. The not only considers priority relationship tasks, but also system. computational decision method IBES based on an improved bald eagle search optimization algorithm designed, which introduces Tent chaotic mapping, Levy Flight mechanism Adaptive weights into to increase initial population diversity, enhance global convergence. simulation results show that 33.07% 22.73% lower than PSO BES, respectively.