Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance for solving expensive optimization problems (EOPs) whose true evaluations are computationally or physically expensive. However, most existing SAEAs only focus on the problems with low dimensionality and they rarely consider solving large-scale EOPs (LSEOPs). To fill this research gap, this article proposes an ensemble surrogate-based coevolutionary optimizer for tackling LSEOPs. First, some local surrogate models are trained with low-dimensional data subsets by using feature selection on the large-scale decision variables, a part of which are used to build a selective ensemble surrogate for better approximating the target LSEOP. Then, a coevolutionary optimizer guided by the ensemble surrogate is designed by running two populations to cooperatively solve the target LSEOP and the simplified auxiliary problem. The information of offspring from the two populations is shared to facilitate the coevolution process, which can exploit the searching experience from the simplified auxiliary problem to help solving the target LSEOP. Finally, an effective infill selection criterion is used to update the ensemble surrogate and enhance its approximate performance. To evaluate the performance of the proposed algorithm, a number of well-known benchmark problems are used and the experimental results validate our superior performance over nine state-of-the-art SAEAs on most cases.

中文翻译：

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用于解决大规模昂贵优化问题的基于集成代理的协同进化算法


替代辅助进化算法（SAEA）在解决昂贵的优化问题（EOP）方面表现出了良好的性能，而这些问题的真正评估在计算或物理上是昂贵的。然而，现有的SAEA大多只关注低维问题，很少考虑解决大规模EOP（LSEOP）。为了填补这一研究空白，本文提出了一种基于集成代理的协同进化优化器来解决 LSEOP。首先，通过对大规模决策变量进行特征选择，使用低维数据子集训练一些局部代理模型，其中一部分用于构建选择性集成代理，以更好地逼近目标 LSEOP。然后，通过运行两个群体来设计由集成代理引导的协同进化优化器，以协作解决目标 LSEOP 和简化的辅助问题。共享两个种群的后代信息以促进共同进化过程，从而可以利用简化辅助问题的搜索经验来帮助解决目标LSEOP。最后，使用有效的填充选择标准来更新集成代理并增强其近似性能。为了评估所提出算法的性能，使用了许多众所周知的基准问题，实验结果验证了我们在大多数情况下优于九个最先进的 SAEA 的性能。