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This paper presents a new differential evolution (DE) to address complex multi-modal function optimization problems. DE is a novel evolutionary approach capable of handling non-differentiable, nonlinear and multi-modal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary algorithm, but sometimes it is inefficient to solve complicated multi-modal problems. In order to improve its search efficiency, a local search procedure is designed and an improved DE (IDE) based on uniform design is proposed. Its performance is tested using some well known benchmark problems. Numerical results show the usefulness of our method.