Quantum-behaved particle swarm optimization dynamic clustering algorithm

This paper proposed a revised quantum-behaved particle swarm optimization algorithm utilizing comprehensive learning strategy to prevent the universal tendency of premature convergence,based on which introduced a novel data clustering algorithm as well.The optimal number of cluster could be automatically obtained by this novel clustering algorithm because a new special coding method for particles was used.Compared with another two dynamic clustering algorithms on five testing data sets,the proposed dynamic clustering algorithm based on the comprehensive learning strategy has the best performance and with the best potential application prospect.