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<p>The driving experience in the modern world is greatly impacted by the advancement of driver-assistance and autonomous technology in Vehicular Ad hoc Networks (VANETs). However, effective data dissemination between the vehicular nodes is highly essential in emergency and normal situations for guaranteeing safety and comfortability in VANETs. In specific, multicast communication need to be greatly prioritised for potentially utilizing the computational resources in an optimal way to achieve the expected degree of Quality of Service (QoS) in VANETs. But this establishment and sustenance of multicast communication is prone to the challenge of frequency topology changes and high mobility that commonly occurs due to the dynamic nature of VANETs. In this paper, Multi-strategy Improved Hybrid Arctic Puffin Optimization Algorithm-based Cluster Multicast Routing Mechanism (MIHAPOACR) is proposed for achieving an efficient and balanced transmission system for addressing the significant factors of coverage, connectivity, load and energy balancing. This MIHAPOACR mechanism is proposed for selecting cluster heads (CHs) to ensure the collection of the sensed data from all the points of target and forward it reliability to the base station for reactive decision making. This hybrid methodology is proposed with the strategies of enhanced tangent flight, Elite initialization and Adaptive t-distribution Mutation for the objective of addressing the limitations of traditional Arctic Puffin Optimization Algorithm (APOA) realized during the process of vehicular nodes’ clustering and CHs selection. It is proposed specifically by replacing levy flight with the merits of tangent search algorithm for the objective of balancing the trade-off between the rate of exploration and exploitation. It further used an adaptive <i>t</i>-distribution mutation strategy for enhancing the optimization potentiality by preventing the limitations of APOA in terms of its susceptibility to local optimal values, insufficient convergence accuracy and poor population diversity.</p>

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Multi-strategy Improved Hybrid Arctic Puffin Optimization Algorithm-based Cluster Multicast Routing for Enhancing Network Stability in VANETs

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • N. Gopinath,
  • Sengathir Janakiraman,
  • V. Vijayagopal,
  • D. Vendhan

摘要

The driving experience in the modern world is greatly impacted by the advancement of driver-assistance and autonomous technology in Vehicular Ad hoc Networks (VANETs). However, effective data dissemination between the vehicular nodes is highly essential in emergency and normal situations for guaranteeing safety and comfortability in VANETs. In specific, multicast communication need to be greatly prioritised for potentially utilizing the computational resources in an optimal way to achieve the expected degree of Quality of Service (QoS) in VANETs. But this establishment and sustenance of multicast communication is prone to the challenge of frequency topology changes and high mobility that commonly occurs due to the dynamic nature of VANETs. In this paper, Multi-strategy Improved Hybrid Arctic Puffin Optimization Algorithm-based Cluster Multicast Routing Mechanism (MIHAPOACR) is proposed for achieving an efficient and balanced transmission system for addressing the significant factors of coverage, connectivity, load and energy balancing. This MIHAPOACR mechanism is proposed for selecting cluster heads (CHs) to ensure the collection of the sensed data from all the points of target and forward it reliability to the base station for reactive decision making. This hybrid methodology is proposed with the strategies of enhanced tangent flight, Elite initialization and Adaptive t-distribution Mutation for the objective of addressing the limitations of traditional Arctic Puffin Optimization Algorithm (APOA) realized during the process of vehicular nodes’ clustering and CHs selection. It is proposed specifically by replacing levy flight with the merits of tangent search algorithm for the objective of balancing the trade-off between the rate of exploration and exploitation. It further used an adaptive t-distribution mutation strategy for enhancing the optimization potentiality by preventing the limitations of APOA in terms of its susceptibility to local optimal values, insufficient convergence accuracy and poor population diversity.