A Metaheuristic Approach for Mission Assignment and Task Offloading in Open RAN-Enabled Intelligent Transport Systems

We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses baseline metaheuristics in both system benefits and mission completion rates, demonstrating strong potential for real-world deployment in dynamic ITS environments.