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Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization

by S.Padmanabhan, Dr.M.Chandrasekaran, Dr. V.Srinivasa Raman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 6
Year of Publication: 2012
Authors: S.Padmanabhan, Dr.M.Chandrasekaran, Dr. V.Srinivasa Raman
10.5120/4691-6826

S.Padmanabhan, Dr.M.Chandrasekaran, Dr. V.Srinivasa Raman . Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization. International Journal of Computer Applications. 38, 6 ( January 2012), 12-18. DOI=10.5120/4691-6826

@article{ 10.5120/4691-6826,
author = { S.Padmanabhan, Dr.M.Chandrasekaran, Dr. V.Srinivasa Raman },
title = { Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 6 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number6/4691-6826/ },
doi = { 10.5120/4691-6826 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:50.608864+05:30
%A S.Padmanabhan
%A Dr.M.Chandrasekaran
%A Dr. V.Srinivasa Raman
%T Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 6
%P 12-18
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traditional mathematical algorithms are incapable of solving real time engineering design problems because of its rigid procedure mainly due to discrete or random data and multi-objective functions in a problem. An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till the optimum or a satisfactory solution is found. There are two population based Swarm inspired methods in computational intelligence areas: Ant colony optimization (ACO) and Particle swarm optimization (PSO). This paper made an attempt to evaluate their performance of these two swarm intelligence techniques. A real engineering application of bevel gear design optimization is considered and results are analyzed with respect to the context.

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Index Terms

Computer Science
Information Sciences

Keywords

Ant Colony Optimization Bevel Gear design Multi Objective Optimization Particle Swarm Optimization.

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