A hybrid fast descent method for globally optimizing high dimensional functions

A new algorithm built on a hybrid method (GRSA) for large scale global optimization problems is proposed. Unlike the previous proposed method that the original objective functions keep unchanged during the whole course of optimizing, a convexized auxiliary function on the obtained local minimizer so far is employed to improve the SA search ability. The experiments conducted show that the new method provides excellent results especially for large scale problems, compared to other state-of-the-art algorithm.