Self adaptive mutation step size in Differential Evolution algorithm

Differential Evolution (DE) is a simple, efficient algorithm which has reportedly outperformed many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, one is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. The proposed scheme dynamically adapts the mutation step size for better exploration and exploitation of the search space. In this paper we propose SaMSDE algorithm that incorporates exponential distributions to produce mutation steps with varying lengths and suitably adjusts the current step length. To show the performance of our proposed SaMSDE, experiments are carried out on a set of seven well-known benchmark problems. Simulation results show that the proposed algorithm can effectively enhance the searching efficiency and greatly improve the searching quality.