A Surrogate-Assisted Variable Grouping Algorithm for General Large Scale Global Optimization Problems
This addresses a key bottleneck in optimization for researchers and practitioners dealing with high-dimensional problems, though it is an incremental improvement over existing decomposition methods.
The authors tackled the problem of decomposing large-scale global optimization problems for cooperative coevolution, proposing a surrogate-assisted variable grouping algorithm that reduces fitness evaluations and improves accuracy. Experimental results on benchmark functions up to 2000 dimensions show SVG has broader applicability and competitive efficiency compared to six state-of-the-art methods.
Problem decomposition plays a vital role when applying cooperative coevolution (CC) to large scale global optimization problems. However, most learning-based decomposition algorithms either only apply to additively separable problems or face the issue of false separability detections. Directing against these limitations, this study proposes a novel decomposition algorithm called surrogate-assisted variable grouping (SVG). SVG first designs a general-separability-oriented detection criterion according to whether the optimum of a variable changes with other variables. This criterion is consistent with the separability definition and thus endows SVG with broad applicability and high accuracy. To reduce the fitness evaluation requirement, SVG seeks the optimum of a variable with the help of a surrogate model rather than the original expensive high-dimensional model. Moreover, it converts the variable grouping process into a dynamic-binary-tree search one, which facilitates reutilizing historical separability detection information and thus reducing detection times. To evaluate the performance of SVG, a suite of benchmark functions with up to 2000 dimensions, including additively and non-additively separable ones, were designed. Experimental results on these functions indicate that, compared with six state-of-the-art decomposition algorithms, SVG possesses broader applicability and competitive efficiency. Furthermore, it can significantly enhance the optimization performance of CC.