Surrogate ensemble assisted large-scale expensive optimization with random grouping

Many fitness evaluations are often needed for large-scale evolutionary optimization to find the optimal solution. Therefore, evolutionary algorithms are impeded to solve computa-tionally expensive problems. Surrogate assisted evolutionary algorithms (SAEAs) have been shown to have good capability in a finite computational budget. However, not many SAEAs, have been proposed for large-scale expensive problems. The main reason is that a proper surrogate model 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 opti-mization 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.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a method that divides large-scale optimization problems into low-dimensional sub-problems using random grouping technique. Surrogate ensembles are trained for each sub-problem to assist optimization, and the best solution found in the sub-problem replaces the best solution found so far for the large-scale problem. Experimental results demonstrate that the proposed method is effective and efficient for solving large-scale expensive optimization problems.