A Memetic Differential Evolutionary Algorithm for High Dimensional Functions´ Optimization

A differential evolutionary (DE) algorithm modified by initialization and local searching is proposed. In the new algorithm, the stochastic properties of chaotic system is used to spread the individuals in search spaces as much as possible, the simplex search method is employed to speed up the local exploiting and the DE operators help the algorithm to jump to a better point. Numerical experiments on benchmark examples including 13 high dimensional functions demonstrate that the new method achieved an improved success rate and final solution with less computational effort.