An enhancement of whale optimization algorithm using ANN for routing optimization in Ad-hoc network

This work presents Ad-hoc optimal route finding with the help of a meta-heuristic algorithm from the group of swarm algorithms named as whale optimization algorithm (WOA) adding new functionality of artificial neural network. This is a multi-objective algorithm that can be helpful in solving many problems. This algorithm copies the hunting behaviour of the humpback whale which is one of the biggest animals on earth and it also includes the functionality of artificial neural network which is based on the concept of biological neural network. Whale optimization algorithm inherits the spiral net technique from hump back whales, in which the whale creates a bubbly spiracle net for encircling the pray and then find the prey either by applying the best find strategy or randomly. This algorithm can be used in many fields like ad-hoc, power electronics, wireless network and classical engineering problems. But here in this paper it is going to apply on route finding problem in ad -hoc network. Where the ad hoc network require to find the location of moving device which keep changing its position and also needed to find the best path from one device to another for data delivery. In such type of problems whale optimization algorithm can be used to produce the batter results then tradition algorithm.