Applying Data Clustering Feature to Speed Up Ant Colony Optimization
Joint Authors
Pang, Chao-Yang
Hu, Ben-Qiong
Zhang, Jie
Hu, Wei
Shan, Zheng-Chao
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-05
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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 N 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 N 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.
American Psychological Association (APA)
Pang, Chao-Yang& Hu, Ben-Qiong& Zhang, Jie& Hu, Wei& Shan, Zheng-Chao. 2014. Applying Data Clustering Feature to Speed Up Ant Colony Optimization. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1014205
Modern Language Association (MLA)
Pang, Chao-Yang…[et al.]. Applying Data Clustering Feature to Speed Up Ant Colony Optimization. Abstract and Applied Analysis No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1014205
American Medical Association (AMA)
Pang, Chao-Yang& Hu, Ben-Qiong& Zhang, Jie& Hu, Wei& Shan, Zheng-Chao. Applying Data Clustering Feature to Speed Up Ant Colony Optimization. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1014205
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1014205