Wu Xunfeng, Lin Qiuzhen, Li Jianqiang, Tan Kay Chen, Leung Victor C M
IEEE Trans Cybern. 2023 Sep;53(9):5854-5866. doi: 10.1109/TCYB.2022.3200517. Epub 2023 Aug 17.
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.
代理辅助进化算法（SAEA）在解决真实评估计算成本高或物理成本高的昂贵优化问题（EOP）方面已展现出良好的性能。然而，大多数现有的SAEA仅关注低维问题，很少考虑解决大规模EOP（LSEOP）。为填补这一研究空白，本文提出一种基于集成代理的协同进化优化器来处理LSEOP。首先，通过对大规模决策变量进行特征选择，利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，设计一个由集成代理引导的协同进化优化器，通过运行两个种群来协同解决目标LSEOP和简化的辅助问题。两个种群的后代信息被共享以促进协同进化过程，这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准则来更新集成代理并提高其逼近性能。为评估所提算法的性能，使用了一些著名的基准问题，实验结果验证了我们在大多数情况下优于九种最先进SAEA的性能。
