Cluster Integrated Updation Strategies for ACO Algorithms

Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilist...

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Bibliographic Details
Published in:International journal of computer applications 2011-01, Vol.30 (2)
Main Authors: S, Raghavendra G, N, Prasanna Kumar
Format: Article
Language:English
Subjects:
Algorithms
Ants
Clusters
Combinatorial analysis
Mathematical models
Optimization
Probability theory
Strategy
Online Access:Get full text
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Summary:Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilistic choices to include good solution component in partially constructed solution, so that better solution can be obtained in the search process. The ant algorithms are typically characterized by co-operation among the ants, greedy, heuristics and feedback approaches that helps them to achieve their goals. In this paper, we propose new updation mechanism based on clustering techniques, which is 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:10.5120/3615-5033