Shelokar, P. S. ; Jayaraman, V. K. ; Kulkarni, B. D. (2004) An ant colony approach for clustering Analytica Chimica Acta, 509 (2). pp. 187-195. ISSN 0003-2670
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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S00032...
Related URL: http://dx.doi.org/10.1016/j.aca.2003.12.032
Abstract
This paper presents an ant colony optimization methodology for optimally clustering N objects into K clusters. The algorithm employs distributed agents which mimic the way real ants find a shortest path from their nest to food source and back. This algorithm has been implemented and tested on several simulated and real datasets. The performance of this algorithm is compared with other popular stochastic/heuristic methods viz. genetic algorithm, simulated annealing and tabu search. Our computational simulations reveal very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Elsevier Science. |
| Keywords: | Ant Colony Metaheuristic; Clustering; Optimization; Euclidean Distance |
| ID Code: | 17156 |
| Deposited On: | 16 Nov 2010 08:19 |
| Last Modified: | 06 Jun 2011 09:04 |
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