###### Abstract  
To reduce computing delay and energy consumption in the Vehicular networks, the total cost of task offloading, namely delay and energy consumption, is studied. A task offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server computing, and cloud computing is proposed. The model not only considers the priority relationship of tasks, but also considers the delay and energy consumption of the system. A computational offloading decision method IBES based on an improved bald eagle search optimization algorithm is designed, which introduces Tent chaotic mapping, Levy Flight mechanism and Adaptive weights into the bald eagle search optimization algorithm to increase initial population diversity, enhance local search and global convergence. The simulation results show that the total cost of IBES is 33.07% and 22.73% lower than that of PSO and BES, respectively. Vehicular networks; mobile edge computing; task collaborative offloading; BES algorithm; computation offloading +
Footnote †: journal: Applied  
game are analyzed theoretically, including optimal offloading response, harvesting energy, transmission power, local calculation rate, etc. The literature [8; 9] mainly studies the minimization of energy consumption. [PERSON] et al. [8] proposed a MEC-assisted offloading scheme for energy-saving tasks by using the cooperative MEC framework. To save energy, a new heuristic hybrid algorithm is proposed to solve the optimization problem. [PERSON] et al. [9] studied a stochastic optimization problem involving dynamic offloading and resource scheduling between local devices, base stations, and back-end clouds. The goal is to minimize the energy and computing resource consumption of energy harvesting (EH) devices in MEC systems while meeting the quality-of-service requirements of IoT devices. To solve this stochastic optimization problem, it is transformed into a deterministic optimization problem, and an online dynamic offloading and resource scheduling algorithm is proposed based on Lyapunov optimization theory. The literature [10; 11; 12] combined optimization of time delay and energy consumption. [PERSON] et al. [10] studied a task offloading strategy for a multi-device, and multi-server system. In order to meet the task requirements of different users, a double deep Q network (double DQN) algorithm is proposed to select strategies and allocate computing resources for task execution positions generated on mobile devices. [PERSON] et al. [11] considered the potentially uneven spatial distribution of mobile devices in MEC networks with multiple wireless edge gateways, allowing one edge gateway to be the first to offload tasks to nearby edge gateways. A joint optimization method for equipment-level and boundary-level task offloading based on deep Q-learning is proposed, and a good balance between task delay and task energy consumption is achieved. [PERSON] et al. [12] constructed a three-layer MEC network architecture, in which mobile devices performed local computation according to task conditions or offloaded it to edge computing nodes and cloud servers. In addition, the binary particle swarm optimization (BPSO) algorithm was used to solve the optimization objective and obtain the optimal offloading strategy. After the above analysis, it can be seen that when global optimization of the system is carried out, the ideal effect can be achieved by considering joint delay and energy consumption. Furthermore, the heuristic algorithm [13; 14; 15] has been well applied in the offloading of mobile edge tasks. [PERSON] et al. [13] proposed a computational offloading strategy based on an improved particle swarm optimization algorithm to solve the delay minimization problem by introducing a cloud server and adding the penalty functions to balance delay and energy consumption. [PERSON] et al.