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Abstract and Applied Analysis
Volume 2014, Article ID 545391, 8 pages
http://dx.doi.org/10.1155/2014/545391
Research Article

Applying Data Clustering Feature to Speed Up Ant Colony Optimization

1College of Computer Science, Sichuan Normal University, Chengdu 610101, China
2College of Management Science, Chengdu University of Technology, Chengdu 610059, China
3Department of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China
4North Sichuan Preschool Educators College, Guangyuan 628000, China
5The Personnel Department of Sichuan Normal University, Chengdu 610068, China

Received 23 January 2014; Accepted 4 April 2014; Published 5 May 2014

Academic Editor: Zhiwu Liao

Copyright © 2014 Chao-Yang Pang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problem so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.