Abstract
The Differential Evolution (DE) algorithm is one of the most effective nature-inspired algorithm that is utilized to solve the complex problems. The computation cost in a DE algorithms increases as the function evaluation is computed for the higher number of generations. In this paper, a dynamic method for selection of parameters has been introduced for basic differential evolution algorithm. The proposed method enhances the convergence speed and performance of the basic differential evolution algorithm. The proposed method is compared with other DE variants such as DE-F, JADEb, DE, DEAE, DEb, DE-PAL, CMAES, MVDE. The results show the effectiveness of proposed method over other methods.
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