ITM Web Conf.
Volume 7, 20163rd Annual International Conference on Information Technology and Applications (ITA 2016)
|Number of page(s)||7|
|Section||Session 5: Algorithms and Simulation|
|Published online||21 November 2016|
An Optimized Ant System For Clustering With Elitist Ant And Local Search
Department of Computer Science ZheJiang University, HangZhou 310000, China
Clustering analysis is an important field in data mining, and also one of the current research hotspots in computer science. This paper focus on some classical data clustering algorithms and swarm intelligence, especially ant colony optimization, trying to combine these two kinds of algorithms and improve the efficiency and accuracy of data clustering. This paper proposes a new ant colony optimization data clustering algorithm, named ant colony clustering algorithm with elitist ant and local search (ACC-EAL). This algorithm adopts a new pheromone incremental calculation method, making the distances among the clusters tend to increase, and the clusters get denser. Meanwhile local search provides the ants more opportunity to find optimal solution and the elite ant strategy makes the ants with optimal solutions contribute more to the pheromone increment.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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