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 execution 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.