An Adaptive Ant Clustering Algorithm

@inproceedings{XiaoHua2006AnAA,
  title={An Adaptive Ant Clustering Algorithm},
  author={Xu Xiao-Hua and Chen Ling},
  year={2006}
}
Enlightened by the behaviors of gregarious ant colonies, an artificial ant movement (AM) model and an adaptive ant clustering (AAC) algorithm for this model are presented. In the algorithm, each ant is treated as an agent to represent a data object. In the AM model, each ant has two states: sleeping state and active state. In the algorithm AAC, the ant’s state is controlled by both a function of the ant’s fitness to the environment it locates and a probability function for the ants becoming… CONTINUE READING
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