Comparison of Ant Colony Optimization Algorithms for Routing Problems in Ad Hoc Network

Abstract

Mobile ad-hoc network (MANET) is a dynamic wireless network which can have fixed or a variable infrastructure. Nodes have the ability to move randomly and arrange themselves in a haphazard order. Multicasting or broadcasting is the type of strategy that can be adopted for the MANETs since they are dynamic in nature. By using Ant Colony Optimization (ACO), performance of mobile ad-hoc networks has and can be improved in numerous ways. Swarm Intelligence which is the study of combined behavior of decentralized and self-organized systems can be an artificial one or a natural one. Ant Colony Optimization (ACO) is one of the most recognized and widely used Swarm Intelligence based routing methodology. In this paper, we have compared different Ant Colony Optimization based algorithms which are Ant Net, Ant-Hoc Net, Ant Routing Algorithm (ARA), and Probabilistic Emergent Routing Algorithm (PERA) on the basis of various parameters such as year of implementation, proposed by, scheme followed, path, types of ants, ant structure, routing table structure, parameters considered in choosing next hop, pheromone evaporation and traffic statistics structure.