A novel ant clustering algorithm based on cellular automata

被引：11
|
作者：
Chen, L [1 ]
Xu, XH [1 ]
Chen, YX [1 ]
He, P [1 ]
机构：
[1] Yangzhou Univ, Dept Comp Sci, Yangzhou 225009, Peoples R China
来源：
IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON INTELLIGENT AGENT TECHNOLOGY, PROCEEDINGS | 2004年
关键词：
cellular automata; swarm intelligence; ant colony algorithm; ants sleeping model;
D O I：
10.1109/IAT.2004.1342937
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the principle of cellular automata in artificial life, an artificial Ants Sleeping Model (ASM) and an ant algorithm for cluster analysis (A(4)C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent data object. In ASM, each ant has two states: sleeping state and active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A(4)C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.
引用
收藏
页码：148 / 154
页数：7
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