Quantum Binary Improved Artificial Bee Colony Algorithm to Solve the Spanning Tree Construction Problem in Vehicular Ad hoc Network

Vehicle Ad hoc Network (VANET), with its characteristics of fast mobility and uneven distribution, adds complexity and uncertainty to the network. To ensure reliable routing connectivity in VANET, it is crucial to tackle challenges such as implementing small-scale and low-precision solutions based on the spanning tree, as well as addressing the absence of effective contingency plans in case of failures. Constructing a large-scale suboptimal spanning tree solution set (LSTs) becomes the key to solving the aforementioned problems. Previous researchers have utilized swarm intelligence optimization algorithms to address the spanning tree construction problem. However, these methods suffer from drawbacks such as low precision, poor scalability, lack of diversity, and uneven distribution. To tackle the aforementioned issues, this paper proposes a Quantum Binary Artificial Bee Colony algorithm (QBABC). Firstly, the Spanning Tree Hit Ratio (SHR) is introduced to evaluate the probability of acquiring a spanning tree in VANET. Secondly, a mathematical model and dataset are constructed based on the considered Quality of Service (QoS) metrics. Then, a Quantum Random Number Generator (QRNG) is proposed, which incorporates a fusion of binary encoding strategies. Finally, a multi-stage search strategy inspired by honeybee behavior is adopted. Through non-parametric statistics and validation with corresponding metrics, the results demonstrate that QBABC exhibits strong competitiveness and provides effective solutions in the event of VANET failures. QBABC’s advantages lie in improving the precision, scalability, diversity, and uniformity of spanning tree construction. This research is of significant importance for enhancing reliable routing connectivity in VANET.