An Improved Whale optimization Algorithm for Cross layer Neural Connection Network of MANET

Abstract

The connecting of numerous remote mobile nodes is known as a mobile ad hoc network. These networks are dynamic and self-contained, allowing them to move about freely. It is referred to as a structure-less network since it lacks a central controller. MANET(Mobile ad hoc network) is one of the most recent developing technologies to gain popularity. This research presents a improved Whale Optimization is enabled for the best feasible solution of the CNCN.The improved whale optimization uses a probability function to determine the best communication path in the network. According to a comparative examination of research, Improved WOA gives significant performance. A two-layer Neural Connection Network model with a cross-layer structure. Physical layer and data link layer. Then in the Physical Layer the load balancing as well as the packet specification is happensso we go for optimization technique. In Data Link Layer the Packet with huge amount of network path is enabled and the packets are delivered with the help of the Connector. The improved whale optimization is enabled in order to achieve the highest level of overall performance such as Waiting time, reliability, failure probability, throughput, and Instantaneous Throughput.