二阶段循环优化差分演化算法

Framework of Recurring Two-Stage Differential Evolution

差分演化算法具有结构简单容易实现,收敛速度快和鲁棒性强等优点,但是存在早熟和进化停滞的现象.提出的二阶段循环优化差分演化算法框架能够很好地保持算法局部开采能力和全局勘探能力的平衡.在差分演化的变异操作中,以马氏距离矩阵为依据分别在目标向量的近邻或者远邻中选择父辈个体参与变异,这样分别形成偏重局部开采或者偏重全局勘探的搜索阶段,此二阶段循环迭代,使得局部开采和全局勘探能力得到震荡平衡.在CEC2005标准函数集上的测试结果显示了提出算法框架的有效性.

The advantages of differential evolution（DE） are its simple structure ,easiness of implement ,fast convergence and robustness .However ,DE often suffers from premature convergence and stagnation problems .A framework of the recurring two-stage DE is proposed to balance global exploration and local exploitation .The proposed framework is based on repeated and alternated ex-ecution of two different stages ,namely ,the local exploitation and global exploration stages .The parent individuals for the mutation operation at each stage are selected from neighbors or strangers of the target vector ,respectively ,based on the Mahalanobis distance matrix .The simulation results on the CEC2005 real-parameter optimization benchmark functions show that the proposed framework can make DE more efficient .