Dynamic Scaling Factor Based Differential Evolution Algorithm

Differential evolution (DE) is a well known and simple population based probabilistic approach used to solve nonlinear and complex problems. It has reportedly outperformed a few evolutionary algorithms when tested over both benchmark and real world problems. DE, like other probabilistic optimization algorithms, has inherent drawback of premature convergence and stagnation. Therefore, in order to find a trade-off between exploration and exploitation capability of DE algorithm, scaling factor in mutation process is modified. In mutation process, trial vector is calculated by perturbing the target vector. In this paper, a dynamic scale factor is proposed which controls the perturbation rate in mutation process. The proposed strategy is named as Dynamic Scaling Factor based Differential Evolution Algorithm (DSFDE). To prove efficiency of DSFDE, it is tested over 10 benchmark problems.