## 1 Introduction  
With the rapid development of automotive technology, the Internet of Vehicles (IoV), as a core component of intelligent transportation systems, is gradually changing people's travel and traffic management modes with the aim to build an efficient, intelligent, and safe transportation ecosystem through the interconnectivity between vehicles and vehicles (V2V), vehicles and infrastructure (V2I), and vehicles and pedestrians (V2P) [1]. In IoV environments, vehicles are equipped with sensors and computing devices that generate large amounts of data and require complex computational task processing. However, the vehicles' own computational resources are often relatively limited to meet the demands of these increasing tasks [2]. Mobile Edge Computing (MEC) [3], as an innovative technology, shows unique advantages in IoV scenarios. It enables vehicles to offloadcomputing-intensive and latency-sensitive tasks to edge nodes for processing by deploying computing resources at base stations or local servers close to the vehicles. This not only effectively alleviates the dilemma of limited computing resources in the vehicle itself but also significantly reduces data transmission latency and energy consumption. However, in the complex and dynamically changing environment of IoVs, computation offloading faces many challenges. On the one hand, the high-speed mobility of vehicles leads to unstable network connections between them and edge servers, and frequent switching and fluctuations in channel quality make computation offloading decisions extremely difficult. On the other hand, since it involves the transmission of sensitive information and critical data of vehicles, it is also crucial to prevent data leakage, tampering, and malicious attacks. To solve complex computational offloading decision optimization problems, traditional heuristic algorithms have been widely used in IoV; for example, Genetic Algorithm (GA) [4] and Ant Colony Optimization (ACO) [5] and its derivatives [6]. These algorithms search for a better computation offloading strategy through specific heuristic rules in a complex solution space to achieve optimization of task scheduling and resource allocation. However, these traditional algorithms often suffer from the defects of slow convergence and easily fall into local optimal solutions. In this regard, the literature [7] proposes an improved Artificial Bee Colony Algorithm (MGABC), which improves the problems of slow convergence and susceptibility to premature convergence in complex optimization problems. The literature [8] proposes a novel hybrid differential evolutionary algorithm with adaptive variance factors and crossover rates designed to balance the global and local search capabilities of the algorithm. While improved algorithms are being proposed, the development of MEC continues to accelerate. The literature [9] proposes an auxiliary architecture for the collaboration of MEC layer and cloud layer, which achieves complementary resource utilization, reduces service latency, and improves resource utilization through task migration mechanism. The literature [10] proposes a Vehicle Fog Edge Computing (VFEC) network architecture, which realizes the cooperation between the computing resources in the MEC layer and the idle resources of the vehicles in the fog layer and improves the system performance. Blockchain technology, with its unique features of decentralization, non-tampering, and traceability, provides a potential solution to the security and trust issues in the IoV computing offloading process [11]. By constructing a distributed ledger structure, blockchain is able to record all the transaction information in the computation offloading process, realize the trusted interaction between vehicles and servers, and enhance the security of the whole system.