A Modified Differential Evolution Algorithm Based on Hybrid Mutation Strategy for Function Optimization

The traditional mutation strategy of differential evolution algorithm can not reach a good balance between the global search and the local search and the operators are constant. The differential evolution algorithm leads to premature convergence and the low search efficiency. Based on analysis of the performance of the optimization strategies,a hybrid mutation strategy is proposed in this paper. The scheme attempts to balance the exploration and exploitation abilities. In this way,emphasis is laid on the global search at the beginning,which results in maintaining the diversity of population. Later,contribution from the local search increases in order to converge to the optimal faster. Meanwhile,the random normal scaling factor F and the time-varying crossover probability factor CR are used synchronously to improve the performance of DE. Finally,the modified differential evolution algorithm is tested on benchmark functions. The simulation results show that the modified algorithm can effectively avoid the premature convergence,as well as modified the global convergence ability and the search efficiency remarkably.