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A Large-Scale Expensive Optimization Algorithm With a Multiview Synthetic Sampling
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AI
DOI：10.1109/TEVC.2025.3571929.png)
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Abstract
En 中文
Many real-world problems involve optimizing numerous decision variables and are expensive to evaluate, known as large-scale expensive optimization problems (LSEOPs). While surrogate-assisted evolutionary algorithms have proven effective for expensive problems, training proper models for LSEOPs remains challenging due to insufficient training data. In this article, we adopt the divide-and-conquer approach, decomposing LSEOPs into lower-dimensional subproblems and constructing models for subproblems, and introduce a multiview synthetic sampling technique for new sample selection. Specifically, we propose sorting all evaluated solutions in an ascending order and dividing them into intervals, from which data are sampled to obtain informative training data for models. The population for the LSEOP is updated by employing cooperative environmental selections on the population, formed by recombining all renewed populations for subproblems to balance exploration and exploitation. Finally, a solution is selected among the current population for the true evaluation based on its multiview performance predicted across all subproblems. Results on CEC’2013 benchmark problems show the effectiveness and efficiency of our proposed method compared to three prevalent large-scale expensive optimization algorithms. Additionally, results on 2000-D CEC’2010 benchmark problems and a 1200-D real-world problem demonstrate encouraging scalability and robustness of the proposed method for addressing higher-dimensional problems.
Keywords:
Divide-and-conquer strategy
infill sampling
large-scale expensive optimization
radial basis function (RBF)
surrogate models
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