An Improved Trigonometric Differential Evolution

Differential evolution is an efficient and powerful population-based stochastic technique capable of handling non-differentiable, non-linear and multi-modal objective functions. In order to improve its performance, this paper introduces a best-trigonometric mutation strategy and applies a crossover rate update strategy to the proposed algorithm. The performance of the proposed algorithm is investigated on a set of benchmark functions. The numerical experimental results show that the convergence rate of proposed algorithm is higher and the robustness of proposed algorithm is better than DE and TDE algorithm.