An adaptive differential evolution algorithm based on logistic model is presented. The algorithm can
automatically adjust scaling factor and crossover factor during the running time, so it can keep the individuals diversity
and improve searching ability of global optimum in the population at the initial generations. However, the algorithm is
gradually stabilized with searching ability of local optimum improved at a later time. Several classic Benchmarks functions
are tested and the results show that the proposed algorithms have fast convergence and higher calculation accuracy.