Mobile Edge Computing (MEC) and fog computing are the key technologies in fifth generation (5 G) networks. In an MEC system, the data of terminal devices can be processed at the edge nodes also known as fog nodes, which can reduce the data transmission from the terminal devices to the cloud, thus reducing the latency and pressure of network traffic. Due to the huge amount of users’ data, a large number of edge nodes need to be deployed. Therefore, we study how to optimally deploy the edge devices on 5G-based small cells (SC) networks based on many-objective evolutionary algorithm (MaOEA). Our goal is to optimize the deployment of edge devices to maximize service quality and reliability, while minimizing cost and energy consumption. This is an NP-hard problem with many objectives. To solve this problem, we propose an improved optimization algorithm named grouping-based many-objective evolutionary algorithm (GMEA). We also compare the performance of GMEA with the state-of-the-art algorithms, and the experimental results demonstrate that GMEA performs better than the other methods in both visualization results and hypervolume (HV) indicators.

中文翻译：

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5G网络边缘设备多目标部署优化

移动边缘计算（MEC）和雾计算是第五代（5G）网络的关键技术。在MEC系统中，终端设备的数据可以在边缘节点（也称为雾节点）进行处理，可以减少终端设备到云端的数据传输，从而降低网络流量的时延和压力。由于用户数据量巨大，需要部署大量的边缘节点。因此，我们研究如何基于多目标进化算法（MaOEA）在基于 5G 的小基站（SC）网络上优化部署边缘设备。我们的目标是优化边缘设备的部署，以最大限度地提高服务质量和可靠性，同时最大限度地降低成本和能源消耗。这是一个具有许多目标的 NP-hard 问题。为了解决这个问题，我们提出了一种改进的优化算法，称为基于分组的多目标进化算法（GMEA）。我们还将 GMEA 的性能与最先进的算法进行了比较，实验结果表明 GMEA 在可视化结果和超体积 (HV) 指标方面的性能都优于其他方法。