A novel ant clustering algorithm based on cellular automata

Abstract

Based on the cellular automata in artificial life, an artificial Ants Sleeping Model (ASM) and an ant algorithm for cluster analysis (A ^4 C) are presented. By simulating the swarm intelligence of the real ant colonies, we use the ant agent to represent the 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 to become active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and consequently the data objects they represent are clustered. Experimental results show that the A ^4 C algorithm on ASM is significantly superior to other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient.