Vehicle edge computing offloading and resource allocation scheme based on particle swarm optimization algorithms

To address the challenge of allocating communication and computational resources in resource-constrained vehicular edge servers, this paper presents an innovative edge computing offloading and resource allocation strategy for intelligent connected vehicles, aimed at maximizing edge server resource utilization. We employ a hybrid optimization approach: first, we tackle the communication resource allocation problem using the Lagrange multiplier method, which constructs a Lagrangian function and solves its dual problem. Next, we apply the Particle Swarm Optimization (PSO) algorithm to address computational resource allocation, leveraging its global search capabilities and rapid convergence to identify optimal resource distribution. Simulation results demonstrate that the proposed scheme significantly enhances edge server resource utilization, exceeding 98%. Additionally, it effectively controls time and energy costs, achieving dual optimization of resource utilization and cost efficiency.