BibTeX:

@article{IJIRSTV4I4021,
     title={Artificial Intelligence Network Load Comparison in Ant Colony Optimization},
     author={M.Sibisakkaravarthi},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={4},
     number={4},
     pages={60--64},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV4I4021.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

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 purpose in optimal time. These ever-changing networks are made possible by the use of pheromones which guide them using a shortest path mechanism. This practice 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 vigorously 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 growing reliability. An implementation of ANT Net based on Marco Dorigo 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 synopsis of how the algorithm perform and how it could be further optimized.

Neural Networks, Optical Language, Multi-Layer, Artificial Intelligence