Surrogate ensemble assisted large-scale expensive optimization with random grouping

is challenging to be trained due to the curse of dimension. In this paper, we propose to employ the random grouping technique to divide a large-scale optimization problem into several low-dimensional sub-problems. Then a surrogate ensemble is trained for each sub-problem to assist the sub-problem optimization. The next parent population for large-scale optimization will be generated by the horizontal composition of the populations for sub-problem optimization. Furthermore, the best solution found so far for the sub-problem with the best population mean fitness value will be used to replace the best solution found so far for the large-scale problem on its corresponding dimensions, and the new solution will be evaluated using the expensive objective function. The experimental results on CEC’2013 benchmark problems show that the proposed method is effective and efficient for solving large-scale expensive optimization problems.