###### Abstract
[14] adopted Genetic algorithm (Ga) based optimization technology to identify the optimal solution of task offloading, and compared it with other offloading strategies using standard data sets and achieved good results. [PERSON] et al. [15] proposed an improved artificial bee colony algorithm (OMABC) to realize the offloading of computing tasks. These heuristic algorithms have certain effects on the task offloading, but it is still necessary to find a better heuristic algorithm to solve the optimization problem. A summary of the above literature studies is summarized in Table 1, where \(\surd\) indicates that there are studies in this area and \(\times\) indicates that there are no studies in this area. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Offloading** & **Time Delay** & \begin{tabular}{c} **Energy** \\ **Consumption** \\ **Optimization** \\ \end{tabular}  & \ \begin{tabular}{c} **Task** \\ **Dependencies** \\ \end{tabular}  & \begin{tabular}{c} **Collaborative** \\ **Offloading** \\ \end{tabular}  \\ \hline Literature [6; 7] & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ Literature [8; 9] & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\ Literature & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) \\
[10; 11; 12] & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ Literature & \(\surd\) & \(\times\) & \(\surdIn summary, most of the above studies on computational offloading strategy problems either optimize only the delay or energy consumption, or do not consider the dependencies between tasks. Some others do not consider the cooperative offloading between vehicles, MEC servers, and cloud servers. In addition, in order to avoid the defects of premature convergence or premature maturity of traditional heuristic algorithms and to improve the global search capability of the algorithms, better heuristics need to be found to solve the optimization problems. Bald Eagle Search (BES) optimization [16] is a new meta-heuristic algorithm proposed by Malaysian scholar [PERSON] in 2020. This algorithm has the advantage of strong global search ability and can effectively solve various complex numerical optimization problems. The task offloading of moving edge computing can be regarded as an optimization problem, so this paper will explore the task offloading of the BES algorithm. Considering the limitations of the above studies together, the main contributions of this paper are as follows:  
* A collaborative computing framework for task offloading is designed, which can offload tasks in local computing, RSU (Road Side Unit) configured with MEC server, and cloud computing, and transform the problem of computing offloading decision into an optimization problem with constraints. According to the relationship between tasks, the priority matrix of task offloading is designed considering the order of task execution;
* The bald eagle search optimization algorithm was improved, the Tent chaos map was introduced to increase the initial population diversity, and the Levy Flight mechanism and adaptive weight were introduced to enhance local search and global convergence;
* Through the simulation, the IBES algorithm is compared with the Local algorithm, MEC algorithm, particle swarm optimization (PSO) algorithm, and BES algorithm. The simulation results show that the IBES algorithm has been better due to improved offloading performance.