ARTIFICIAL INTELLIGENCE NETWORK LOAD BALANCING USING ANT COLONY OPTIMIZATION
Ants first evolved around 120 million years ago, took form in over 11,400 different species, and are considered one of the most successful insects due to their highly organised 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 behaviour of ants in the early 1990's to discover new routing algorithms. The result of these studies is Ant Colony Optimisation (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 minimise the number of nodes that a call will need to connect to in order to be completed thus; minimising network load and increasing reliability. An implementation of ANTNet based on Marco Dorigo and Thomas Stützle has been designed, and through this a number of visually aided tests were produced to compare the genetic algorithm to a non-generic algorithm. The report will finally conclude with a summary of how the algorithm performs and how it could be further optimised.
Electronic communication networks can be categorised as either circuit-switched or packet-switched. Circuit-switched networks rely on a dedicated connection from source to destination, which is made once at start-up and remains constant until the tear-down of the connection. An example of a circuit switched network would be the British Telecoms telephone network. Packet-switched networks work quite differently, however, and all data to be transmitted is divided into segments and sent as data-packets. Data-packets can arrive out of order in a packet-switched network, with a variety of paths taken through different nodes in order to get to their destination. The internet and office local area networks are both good examples of packet-switched networks.
A number of techniques can be employed to optimise the flow of traffic around a network. Such techniques include flow and congestion control, where nodes send packet acknowledgements from destination nodes to either ramp-up or decrease packet transmission speed. The area of interest in this report concentrates on the idea of network routing and routing tables. These tables hold information used by a routing algorithm to make a local forwarding decision for the packet on the next node it will visit in order to reach its final destination.
One of the issues with network routing (especially in very large networks such as the internet) is adaptability. Not only can traffic be unpredictably high, but the structure of a network can change as old nodes are removed and new nodes added. This perhaps makes it almost impossible to find a combination of constant parameters to route a network optimally.
Table of Content——–vii
1.0 Introduction ——-1
1.1 Statement of Problem——4
1.2 Purpose of the Study——5
1.3 Significance of Study——8
1.5 Scope of Study——-11
2.0 Review of Related Literature —-12
2.6 Summary of Literature Review—- 19
3.0 Research Methodology and Procedure—22
3.1 Population ——–22
3.2 Sample and Sampling Technique—-22
3.3 Validation of the Instrument —-23
3.4 Reliability of the Instrument —–23
3.5 Data Analysis. 3.5 Data Analysis.——-23
4.0 Presentation and Discussion of Result—24
4.1 Analysis and interpretaion of Data—25
4.2 Discussion of Results——38
5.0. Summary, Conclusion, and Recommendation –40