Efficient Clustering and Routing using Multi-Objective Dynamic Osprey Optimization Algorithm in Vehicular Ad-hoc Network

Vehicular Ad Hoc Network (VANET) is a developing technology applied in Intelligent Transportation Systems. The existing research has limitations, such as network instability due to vehicles' mobile nature, which reduces the network lifetime. To overcome this problem, this research introduced a Multi-objective Dynamic Osprey Optimization Algorithm (MDOOA) based on energy-efficient clustering and routing in VANET. In the exploration phase, a dynamic, elite guidance mechanism with an adjustable ratio strategy is used to enhance individuals' search space exploration and avoid local optima issues at the beginning of the iteration. The MDOOA is used for Cluster Head (CH) and route selection with objective functions like the distance between neighbor nodes, the distance between Base Station (BS) to CH, energy, centrality and Load Balancing Factor (LBF), which gradually contributes to enhancing Network Lifetime (NLT). The CH maintenance is also performed using MDOOA to balance loads among clusters, which is required to prevent node failure. The delay, Packet Delivery Ratio (PDR), NLT, Energy Consumption (EC) and throughput are considered for experimental analysis of MDOOA. The experimental results show improvement in delay, Packet Delivery Ratio (PDR), NLT, Energy Consumption (EC) and throughput when compared with the existing techniques.