The differential evolution algorithm is robust

easy to use

requires few control parameters

and has various optimization strategies.Based on analysis of advantages and disadvantages of these optimization strategies

a modified differential evolution algorithm with hybrid optimization strategy is proposed.The main idea of the modified differential evolution algorithm is to divide all of the individuals into two groups randomly

and the two groups adopt different optimization strategies.The convergence speed and search succeed probability of the modified differential evolution are tested using five benchmark functions for optimization algorithm

and the results are compared with dynamic differential evolution and particle swarm optimization.From the simulation results

it is observed that the search efficiency of the modified differential evolution is significantly improved as well as the high search succeed probability is ensured.