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Differential evolution (DE) is a simple and efficient scheme for global optimization over continuous spaces. DE is generally considered as a reliable, accurate, robust and fast optimization techniques. It outperforms many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a new differential evolution algorithm based on adaptive control parameters (ACDE) is introduced. The performance of ACDE algorithm is investigated with ten standard benchmark problems and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical results show that the ACDE algorithm outperforms the classical DE in terms of all considered performance measures.