State Information-based Ant Colony Clustering Algorithm

State information-based ant colony clustering algorithm is proposed in the paper. The data object is denoted as an ant which has behaviors such as moving or sleeping, the state information´s influence on the ants´ behaviors is paid more attention. The reference value of ants´ information in the static and active state is increased or decreased respectively. State information is taken as the important computing parameter of fitness and active probability of ants, therefore, it could carry out self-adaptive updates with the running of the algorithm, the concept of sensation threshold is introduced in order to avoid the frequent computation and update of the state information and improve the performance and the self-adaptation level of ant colony clustering algorithm.