A Hybrid Multiagent Adaptive Clustering Algorithm Using Whale Optimization in VANETs Network

Mainly to improve the efficiency to reduce the power utilization in earlier researches the clustering model is concentrated. It is the formation of clusters in the network which increases the connection degree fundamentally and with the leaders the network stability is also increased. With the presence of a huge number of devices even the earlier clustering models consists of certain drawbacks like data loss and delay. Overcome such drawbacks in this article a hybrid multiagent adaptive algorithm with whale optimization (HMACWO) is developed so that an optimal clustering is introduced which is able to increase the efficiency of the vehicular network. The core modules which are present in this article are an efficient clustering process and whale optimization with an improved clustering model. Experimental demonstration of this model is done in NS3 software and using certain parameters such as the cluster efficiency, CH lifetime, packet delivery ratio, network throughput, and average delay the performance of the network is analyzed. From the results, it is shown that the HMACWO attains a maximum cluster efficiency and $\mathbf{C H}$ lifetime when compared with the earlier methods.