Abstract: Ants first evolved around 120 million years ago, take form in over 11,400 different species and are considered one of the most successful insects due to their highly organized colonies, sometimes consisting of millions of ants. One particular notability of ants is their ability to create "ant streets". Long, bi-directional lanes of single file pathways in which they navigate landscapes in order to reach a destination in optimal time. These ever-changing networks are made possible by the use of pheromones which guide them using a shortest path mechanism. This technique allows an adaptive routing system which is updated should a more optimal path be found or an obstruction placed across an existing pathway. Computer scientists began researching the behavior of ants in the early 1990's to discover new routing algorithms. The result of these studies is Ant Colony Optimization (ACO) and in the case of well implemented ACO techniques, optimal performance is comparative to existing top-performing routing algorithms. This article details how ACO can be used to dynamically route traffic efficiently. An efficient routing algorithm will minimize the number of nodes that a call will need to connect to in order to be completed thus; minimizing network load and increasing reliability. An implementation of ANTNet based on Marco Dorigo & Thomas stutzle has been designed and through this a number of visually aided test were produced to compare the genetic algorithm to a non-generic algorithm. The report will final conclude with a summary of how the algorithm perform and how it could be further optimized.
Keywords: Algorithm, Ant Colony Optimization, Networks, Routing, Traffic.
Title: Network Load Balancing Using Ant Colony Optimization
Author: Mr. Ujwal Namdeo Abhonkar, Mr. Swapnil Mohan Phalak, Mrs. Pooja Ujwal Abhonkar
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals
