Over the last few decades, a considerable number of Differential Evolution (DE) algorithms have been proposed with excellent performance on mathematical benchmarks. However, like any other optimization algorithm, the success of DE is highly dependent on its search operators and control parameters. Although a considerable number of investigations have been carried out for parameter selection, it is seen as a tedious task. In this paper, we propose a DE algorithm that uses an adaptive mechanism to select the best performing combination of parameters (amplification factor, crossover rate and the population size) during the course of a single run. The performance of the algorithm is analyzed on a set of 24 constrained optimization test problems. The results demonstrate that the proposed algorithm not only saves the computational time, but also shows better performance over the state-of-the-art algorithms.