A Teaching and Learning Based Optimization Algorithm for Efficient Routing in Vehicular Ad Hoc Networks

Many technologies offered by Intelligent Transportation Systems (ITS), such as encrypted financial transactions, media applications, and effective traffic control, depend on Vehicular Ad Hoc Networks (VANETs). Vehicular network topology is always changing as a result of increased traffic, and network scalability is limited by sparse network dispersion. As a result, maintaining a steady, ideal path is difficult in the network, which increases instability. This study presented Teaching and Learning based Optimisation (TLO), which generates optimal routing in vehicular communication. The results were compared with existing techniques including the Whale Optimisation Algorithm (p-WOA), Harris Hawks Optimisation (HHO) and Multi-Objective Optimisation Algorithm (MOOA). The proposed technique achieved better results in terms of number of Number of alive nodes, including Latency, and Throughput respectively.