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In this paper, an improved hybrid differential evolution algorithm (IHDE) is proposed for nonlinear and mixed-integer nonlinear programming models (NLPs and MINLPs) in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE takes full advantage of differential information and global statistical information extracted respectively by differential evolution algorithm (DE) and the annealing mechanism-embedded estimation of distribution algorithm (EDA), and adopts the feasibility rules to handle constraints. Simulation and comparison based on three bench-marks and a practical scheduling of crude oil blending problem demonstrate the efficiency, accuracy and robustness of IHDE. Moreover, the key parameters of IHDE are also analyzed.