The fifth generation (5G) network technology has made it possible to further exploit the radio spectrum and allow a large number of devices to concurrently have access to the mobile internet. The network has become a viable option for creating such connections and providing Internet of Things (IoT) services in a fast, secure and reliable way. In micro cloud computing, Edge computing refers to a term that describes the edge computing technology. It is expected that the IoT would utilize edge computing to minimize offloading tasks and latency as well as use the computing power in the offloading process. Although edge computing is an old technology, its role in facilitating the real-time transfer of data from devices to the cloud and the real-time processing of data within the devices has only been realized in recent times. In the process of offloading data and computing tasks, the data flow may be interrupted. The 5G technology can provide a better solution to help IoT applications close the gap between edge and the limited device resources, thus making it more reliable. This paper employs the Bald eagle search (BES) algorithm, particle swarm optimization algorithm, and genetic algorithm to simulate the edge computing. The goal is to determine which of the algorithms has the best performance, based on their latency and offloading capacity, in edge computing by comparing their results. On the basis of the execution time, amount of resources utilized in offloading tasks, and total cost of vehicular edge, the three algorithms are compared and validated. According to the simulation results, the best-performing method is the BES algorithm, as it gives the IoT quick access to information.

第五代（5G）网络技术使进一步开发无线电频谱成为可能，并允许大量设备同时访问移动互联网。网络已成为创建此类连接和以快速、安全和可靠的方式提供物联网 (IoT) 服务的可行选择。在微云计算中，边缘计算是指描述边缘计算技术的术语。预计物联网将利用边缘计算来最小化卸载任务和延迟，并在卸载过程中使用计算能力。虽然边缘计算是一项古老的技术，但它在促进数据从设备到云端的实时传输以及设备内数据的实时处理方面的作用直到最近才得以实现。在卸载数据和计算任务的过程中，数据流可能会中断。5G技术可以提供更好的解决方案，帮助物联网应用缩小边缘与有限设备资源之间的差距，从而使其更加可靠。本文采用秃鹰搜索（BES）算法、粒子群优化算法和遗传算法来模拟边缘计算。目标是通过比较它们的结果，根据它们的延迟和卸载能力，确定哪种算法在边缘计算中具有最佳性能。根据执行时间、卸载任务使用的资源量和车辆边缘的总成本，对三种算法进行比较和验证。根据仿真结果，性能最好的方法是BES算法，