Journal of Global Optimization
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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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.