Unsupervised Updation Strategies for ACO Algorithms

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 o...

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Bibliographic Details
Published in:International journal of computer applications 2011-01, Vol.30 (11)
Main Authors: S, Raghavendra G, N, Prasanna Kumar
Format: Article
Language:English
Subjects:
Algorithms
Exact solutions
Iterative methods
Learning
Mathematical analysis
Mathematical models
Optimization
Strategy
Online Access:Get full text
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Summary: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.
ISSN:0975-8887
0975-8887
DOI:10.5120/3694-5118