KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification

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In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.
Submitted to ALIFE XI

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Neural and Evolutionary Computing, Computer Vision and Pattern Recognition

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http://salesiana.dossiersoluciones.com/handle/123456789/157085

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0803.2695
 http://arxiv.org/abs/0803.2695

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arXiv