A novel modified differential evolution algorithm f or clustering

Differential evolution (DE) is easy to trap into lo cal optima. In this paper, a modified differential evolution algorithm (MDE) proposed to speed the convergence r ate of DE and enhance the global search of DE. The MDE employed a new mutation operation and modified cros sover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum e In this work, firstly, we employed a new strategy t o dynamic adjust mutation rate (MR) and crossover r ate (CR), which is aimed at further improving algorithm perfo rmance. Secondly, the MDE algorithms are used for d clustering on several benchmark data sets. The perf ormance of the algorithm based on MDE is compared w ith DE algorithms on clustering problem. The simulation re sults show that the proposed MDE outperforms the ot her two algorithms in terms of accuracy, robustness and con vergence speed.