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Reseach Article

Performance Analysis of a Telecommunication Network using Swarm Intelligence

by Karuna Rani, Meenu Vijarania
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 13
Year of Publication: 2013
Authors: Karuna Rani, Meenu Vijarania
10.5120/13580-0339

Karuna Rani, Meenu Vijarania . Performance Analysis of a Telecommunication Network using Swarm Intelligence. International Journal of Computer Applications. 78, 13 ( September 2013), 1-5. DOI=10.5120/13580-0339

@article{ 10.5120/13580-0339,
author = { Karuna Rani, Meenu Vijarania },
title = { Performance Analysis of a Telecommunication Network using Swarm Intelligence },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 13 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number13/13580-0339/ },
doi = { 10.5120/13580-0339 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:26.778893+05:30
%A Karuna Rani
%A Meenu Vijarania
%T Performance Analysis of a Telecommunication Network using Swarm Intelligence
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 13
%P 1-5
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Swarm Intelligence, a mutant of biological swarms, exhibits numerous powerful features which are described in communication networks. With the increase in size and complexity of network, performance analysis is becoming difficult. With scalability of network, it becomes difficult to manage the route and indicate which one is the best. The intent of this paper is to find optimal path for routing of calls in a telecommunication network and achieve Load Sharing. For finding optimal path, the pheromone laying and sensing property of natural swarms is used. In this research work we present a simulated network model that uses artificial ants and simulated pheromone for finding optimal path from source to destination. Next node selection is done on the basis of local pheromone distribution and pheromone updation takes place based on the congestion encountered on the node. Simulation results using ACO mode will be compared with non-ACO mode using graphs.

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Index Terms

Computer Science
Information Sciences

Keywords

Swarm Intelligence Ant Colony Optimization(ACO) Mobile Agents Multiple Ant Colony Optimization(MACO) Set Covering Problem(SCP).