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Differential Evolution (DE) is one of the most powerful global numerical optimization algorithms in the field of evolutionary algorithm. However, the performance of DE is affected by control parameters and mutation strategies. In addition, the choice of the control parameters and mutation strategies is strongly dependent on the characteristics of optimization problems. As a result, studies focused...
Differential evolution (DE) is a simple yet efficient evolutionary algorithm. Because of its simplicity, effectiveness and robustness, DE has gradually become more popular and applied in various fields. In addition, a lot of works have been done to improve the search ability of DE. Among them, opposition-based DE (ODE), which is incorporated opposition-based learning (OBL), has shown better performance...
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