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．