Various works have been proposed to solve expensive multiobjective optimization problems (EMOPs) using surrogate-assisted evolutionary algorithms (SAEAs) in recent decades. However, most existing methods focus on EMOPs with less than 30 decision variables, since a large number of training samples are required to build an accurate surrogate model for high-dimensional EMOPs, which is unrealistic for expensive multiobjective optimization. To address this issue, we propose an SAEA with an adaptive dropout mechanism. Specifically, this mechanism takes advantage of the statistical differences between different solution sets in the decision space to guide the selection of some crucial decision variables. A new infill criterion is then proposed to optimize the selected decision variables with the assistance of surrogate models. Moreover, the optimized decision variables are extended to new full-length solutions, and then the new candidate solutions are evaluated using expensive functions to update the archive. The proposed algorithm is tested on different benchmark problems with up to 200 decision variables compared to some state-of-the-art SAEAs. The experimental results have demonstrated the promising performance and computational efficiency of the proposed algorithm in high-dimensional expensive multiobjective optimization.

近几十年来，已经提出了各种工作来使用替代辅助进化算法（SAEA）解决昂贵的多目标优化问题（EMOP）。但是，大多数现有方法集中于决策变量少于30个的EMOP，因为需要大量的训练样本才能为高维EMOP建立准确的替代模型，这对于昂贵的多目标优化来说是不现实的。为了解决此问题，我们提出了一种具有自适应辍学机制的SAEA。具体来说，此机制利用了决策空间中不同解决方案集之间的统计差异来指导某些关键决策变量的选择。然后，提出了一个新的填充标准，以借助替代模型来优化所选的决策变量。而且，将优化的决策变量扩展到新的全长解决方案，然后使用昂贵的函数评估新的候选解决方案以更新档案。与某些最新的SAEA相比，该算法在多达200个决策变量的不同基准问题上进行了测试。实验结果证明了该算法在高维代价高昂的多目标优化中的有希望的性能和计算效率。