a novel differential evolution using a mixed mutation strategy
Differential Evolution (DE) is well-known as a simple and efficient evolutionary algorithm for global optimization problems. However, the mutation strategies used in DE greatly affect its performance. Although many mutation operators have been proposed in DE, for each operator there are some types of optimization problems that cannot be solved efficiently. In this paper, we propose a novel DE using a mixed mutation strategy (MMSDE), which integrate four different mutation operators, may be able to overcome the shortcomings of a pure strategy. In order to verify the performance of MMSDE, we test it on 8 famous benchmark functions. The simulation results show that MMSDE performs equally well or better than classical DE, modified DE (MoDE) and trigonometric mutation DE (TDE) on all of the test problems.
mutation strategy differential evolution (de) evolutionary algorithm optimization