A differential evolution SAF-DE algorithm which jumps out of local optimal

The principle of differential evolutionary algorithm is easy to understand, and it has the advantages of fast convergence, simple operation and good stability, which has been favored by many researchers. However, the differential evolution algorithm is easy to fall into the local optimum, and even cause the algorithm to stagnate, the low efficiency, and the unstable convergence speed of algorithm. This paper proposes an improved differential evolution (SAF-DE) algorithm, which uses the perturbation formula to perturb the individual values in the population to make individual more diversified. So as to achieve the purpose of improving the accuracy and convergence speed in the optimization process of the differential evolution algorithm. algorithm, the improved algorithm has higher convergence speed and accuracy on some standard functions.