Many-Objective Edge Computing Server Deployment Optimization for Vehicle Road Cooperation

In the Internet of Vehicles (IoV), vehicles need to process a large amount of perception data to support tasks such as road navigation and autonomous driving. However, their computational resources are limited. Therefore, it is necessary to explore the combination of vehicle-road cooperation with edge computing. Roadside units (RSUs) can provide data access services for vehicles, and deploying edge servers on RSUs can improve the data processing capability in IoV environments and ensure the sustainability of vehicle communications, thus supporting complex traffic scenarios more effectively. In this work, we study the deployment of RSUs in vehicle-road cooperative systems. To balance the deployment cost of RSUs and the quality of service (QoS) of vehicle users, we propose an RSU deployment optimization model with six objectives, including time delay, energy consumption and security when vehicles offload their tasks to RSUs, as well as load balancing and the number and communication coverage area of RSUs. In addition, we propose a Wasserstein generative adversarial network (WGAN)-based Two_Arch2 (WGTwo_Arch2) to solve this many-objective optimization problem to better ensure the diversity and convergence of the solutions. In addition, a polynomial variation strategy based on Lecy's flight mechanism and a diversity archive selection strategy with an adaptive Lp-norm are also proposed to balance the exploratory and exploitative capabilities of the algorithm. The effectiveness of the proposed algorithm WGTwo_Arch2 for 6-objective RSU deployment optimization is verified by comparisons with five different algorithms.