A surrogate-assisted variable grouping algorithm for general large-scale global optimization problems.

• A new separability detection criterion possessing broad applicability is designed. • An efficient surrogate-assisted scheme is introduced to seek the global optimum of a variable required by the new criterion. • A dynamic-binary-tree-based variable grouping procedure is developed to conduct the problem decomposition. • Experimental studies on a more general benchmark suite verify the effectiveness of the proposed algorithm. Problem decomposition plays an important role when applying cooperative coevolution (CC) to large-scale global optimization problems. However, most learning-based decomposition algorithms only apply to additively separable problems, while the others insensitive to problem type perform low decomposition accuracy and efficiency. Given this limitation, this study designs a general-separability-oriented detection criterion, and further proposes a novel decomposition algorithm called surrogate-assisted variable grouping (SVG). The new criterion detects the separability between a variable and some other variables by checking whether its optimum changes with the latter. Consistent with the definition of general separability, this criterion endows SVG with strong applicability and high accuracy. To reduce expensive fitness evaluations, SVG locates the optimum of a variable with the help of a surrogate model rather than the original high-dimensional model. Moreover, it converts the variable-grouping process into a search process in a binary tree by taking variable subsets as tree nodes. This facilitates the reutilization of historical separability information, thereby reducing separability detection times. Experimental results on a general benchmark suite indicate that compared with six state-of-the-art decomposition algorithms, SVG achieves higher accuracy and efficiency on both additively and nonadditively separable problems. Furthermore, it can significantly enhance the optimization performance of CC. [ABSTRACT FROM AUTHOR]