Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space.

中文翻译：

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一种基于决策空间压缩的昂贵多目标优化算法

在过去的几年中，提出了许多代理辅助昂贵的多目标优化算法来处理昂贵的多目标优化问题。随着决策变量数量的增加，代理模型的准确性会降低。在本文中，我们提出了一种基于决策空间压缩的代理辅助昂贵的多目标优化算法。在低维压缩空间中构建了几个代理模型。在较低压缩的决策空间中生成和选择有希望的点，并解码到原始决策空间进行评估。实验研究表明，该算法在处理具有高维决策空间的昂贵多目标优化问题方面取得了良好的性能。