Self-adaptive multi-purpose Grey Wolf optimization for efficient routing of wireless ad hoc networks

Abstract

One of the well-known issues in the field of network routing is the Shortest Path Routing (SPR). The objective is to find the least-cost path with minimum delay and link breaks. Even though there are many algorithms to solve SPR, the cost, as well as link breaks, are indeed more thought-provoking in the real-time application. This paper intends to develop a routing approach that solves the challenges like route establishment and route recovery. The selection of the optimal route is done by adopting a generalized multi-purpose optimization algorithm named Grey Wolf Optimizer. Along with this, this paper adopts Neural Network (NN) to predict the node movements in the ad hoc network. The proposed routing algorithm is compared to the conventional approaches, and the significance of the approach is described clearly.