EJAIoV: Enhanced Jaya Algorithm-Based Clustering for Internet of Vehicles Using Q-Learning and Adaptive Search Strategies

The Internet of Vehicles (IoV) is an indispensable part of contemporary Intelligent Transportation Systems (ITS), providing efficient vehicle-to-everything (V2X) communication. Nevertheless, high mobility and consequent topological changes in IoV networks create overwhelming difficulties in establishing and maintaining stable and effective communication. In this work, we introduce the Enhanced Jaya Algorithm for IoV (EJAIoV), an optimized clustering algorithm using optimization to develop stable and long-term clusters in IoV scenarios. EJAIoV uses efficient random initialization with three scrambling strategies to produce diverse, high-quality solutions. Q-learning selection between three neighborhood operators enhances local search effectiveness by incorporating a segmented operator. In addition, an adaptive search balance strategy adjusts solution updating dynamically to avoid premature convergence and optimize the exploration procedure. Simulation experiments show that EJAIoV outperforms existing clustering algorithms, achieving up to 31.5% improvement in cluster lifetime and 28.2% reduction in the number of clusters across various node densities and grid sizes.

@article{LU2025,
  title     = {EJAIoV: Enhanced Jaya Algorithm-Based Clustering for Internet of Vehicles Using Q-Learning and Adaptive Search Strategies},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jinchuan LU},
  doi       = {10.14569/IJACSA.2025.0160560},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160560}
}