Adaptive scenario subset selection for worst-case optimization and its application to well placement optimization
Adaptive scenario subset selection for worst-case optimization and its application to well placement optimization
复制标题
最坏情况优化的自适应场景子集选择及其在井位优化中的应用
DOI：
10.1016/j.asoc.2022.109842
复制
发表时间：
2023
影响因子：
8.7
通讯作者：
Akimoto Youhei
中科院分区：
文献类型：
--
作者：
Miyagi Atsuhiro;Fukuchi Kazuto;Sakuma Jun;Akimoto Youhei
在这项研究中，我们考虑基于模拟的最坏情况下的优化问题，连续的设计变量和有限的场景集。为了减少所需的模拟次数，并增加重新启动的次数，以获得更好的局部最优解，我们提出了一种新的方法称为自适应场景子集选择（AS 3）。所提出的方法子采样的场景子集作为支持，以构建最坏情况下的目标函数在一个给定的邻域，我们引入这样的场景子集。此外，我们还提出了一种新的优化算法，将AS 3与协方差矩阵自适应进化策略（CMA-ES）相结合，称为AS 3-CMA-ES。在每次算法迭代时，选择支持场景的子集，并且CMA-ES尝试优化仅通过场景的子集计算的最坏情况目标。所提出的算法减少了所需的模拟执行模拟的情况下，只有一个子集，而不是对所有的情况。在数值实验中，我们验证了当支持场景数与场景总数的比率相对较小时，AS 3-CMA-ES在模拟次数方面比蛮力方法和代理辅助方法lq-CMA-ES更有效。此外，还评价了AS 3-CMA-ES在二氧化碳捕集与封存（CCS）优化布井中的应用。与蛮力方法和lq-CMA-ES相比，AS 3-CMA-ES能够找到更好的解决方案，因为更频繁的重新启动。
In this study, we consider simulation-based worst-case optimization problems with continuous design variables and a finite scenario set. To reduce the number of simulations required and increase the number of restarts for better local optimum solutions, we propose a new approach referred to asadaptive scenario subset selection(AS3). The proposed approach subsamples a scenario subset as a support to construct the worst-case objective function in a given neighborhood, and we introduce such a scenario subset. Moreover, we develop a new optimization algorithm by combiningAS3and the covariance matrix adaptation evolution strategy (CMA-ES), denotedAS3-CMA-ES. At each algorithmic iteration, a subset of support scenarios is selected, and CMA-ES attempts to optimize the worst-case objective computed only through a subset of the scenarios. The proposed algorithm reduces the number of simulations required by executing simulations on only a scenario subset, rather than on all scenarios. In numerical experiments, we verified thatAS3-CMA-ESis more efficient in terms of the number of simulations than the brute-force approach and a surrogate-assisted approachlq-CMA-ESwhen the ratio of the number of support scenarios to the total number of scenarios is relatively small. In addition, the usefulness ofAS3-CMA-ESwas evaluated for well placement optimization for carbon dioxide capture and storage (CCS). In comparison with the brute-force approach andlq-CMA-ES,AS3-CMA-ESwas able to find better solutions because of more frequent restarts.