SURROGATE'S OPTIMA ASSISTED EVOLUTIONARY ALGORITHM FOR OPTIMIZATION OF EXPENSIVE PROBLEMS

In this paper, an efficient surrogate's optima assisted evolutionary optimization algorithm is proposed for the optimization of computationally expensive problems, which sometimes involve costly simulation analysis. The proposed algorithm uses the global optimum and local optima of the surrogates to speed up the evolutionary optimization process. Moreover, the optimization efficiency of the proposed algorithm can be enhanced by using a surrogate prescreening strategy. In order to validate the proposed algorithm, it is tested on several common numerical benchmark problems of 30 dimensions and compared with several optimization algorithms. The results show that the proposed algorithm is very promising for the optimization of the expensive problems.