JOURNAL OF COMPUTING, VOLUME 2, ISSUE 2, FEBRUARY 2010, ISSN 2151-9617
Nature inspired artificial intelligence based adaptive
traffic flow distribution in computer network
Manoj Kumar Singh
Abstract— Because of the stochastic nature of traffic requirement matrix, it’s very difficult to get the optimal traffic
distribution to minimize the delay even with adaptive routing protocol in a fixed connection network where capacity
already defined for each link. Hence there is a requirement to define such a method, which could generate the
optimal solution very quickly and efficiently. This paper presenting a new concept to provide the adaptive optimal
traffic distribution for dynamic condition of traffic matrix using nature based intelligence methods. With the defined
load and fixed capacity of links, average delay for packet has minimized with various variations of evolutionary
programming and particle swarm optimization. Comparative study has given over their performance in terms of
converging speed. Universal approximation capability, the key feature of feed forward neural network has applied to
predict the flow distribution on each link to minimize the average delay for a total load available at present on the
network. For any variation in the total load, the new flow distribution can be generated by neural network
immediately, which could generate minimum delay in the network. With the inclusion of this information,
performance of routing protocol will be improved very much.
Index Terms—flow distribution, computer network, evolutionary programming, particle swarm optimization,
artificial neural network.
THE designing of computer network is always a
challenging and fascinating task. Because, several
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difficulties are manifold compare to earlier days of
network evolution. But characteristics of...