Solving multi-center dynamic optimization problems using modified differential evolution

A novel self-learning differential evolution is proposed to solve multi-center optimization under dynamic environments. The approach of Re-evaluating is used to monitor environmental changes, then historial best individual obtained the environment guides population to new environment. What's more, the self-learning mechanism is employed to reduce the impact of dynamic changes of environment.The experimental results on a set of 4 test dynamic functions show that, self-learning differential algorithm outperforms other algorithms in term of the convergence speed.