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Unsupervised Updation Strategies for ACO Algorithms
by
Raghavendra G S,
Prasanna Kumar N
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 30 - Number 11 |
| Year of Publication: 2011 |
| Authors: Raghavendra G S, Prasanna Kumar N |
10.5120/3694-5118
|
Raghavendra G S, Prasanna Kumar N . Unsupervised Updation Strategies for ACO Algorithms. International Journal of Computer Applications. 30, 11 ( September 2011), 37-43. DOI=10.5120/3694-5118
@article{
10.5120/3694-5118,
author = {
Raghavendra G S,
Prasanna Kumar N
},
title = { Unsupervised Updation Strategies for ACO Algorithms },
journal = {
International Journal of Computer Applications
},
issue_date = { September 2011 },
volume = { 30 },
number = { 11 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = {
37-43
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume30/number11/3694-5118/
},
doi = { 10.5120/3694-5118 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:54.842922+05:30
%A Raghavendra G S
%A Prasanna Kumar N
%T Unsupervised Updation Strategies for ACO Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 11
%P 37-43
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Ant Colony Optimization (ACO) algorithms belong to class of metaheuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to the optimized solution. In this paper, we propose new updation mechanism based on clustering techniques, an unsupervised learning mechanism aimed at exploring the nearby solutions region. We also report in detail the impact on performance due to integration of cluster and ACO.
References
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Keywords