A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems
A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems
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DOI：
10.1080/00207721.2011.602482
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发表时间：
2012-07
影响因子：
4.3
通讯作者：
Y. Tenne;K. Izui;S. Nishiwaki
中科院分区：
文献类型：
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作者：
Y. Tenne;K. Izui;S. Nishiwaki
许多现实世界的优化问题依赖于计算昂贵的模拟来评估候选解决方案。通常，这样的问题将包含模拟失败的候选解决方案，例如，由于模拟的限制。这样的候选解决方案可能会阻碍优化的有效性，因为它们可能会消耗大部分的优化预算，而不会向优化器提供新的信息，从而导致搜索停滞和较差的最终结果。现有的方法来处理这样的设计要么完全放弃他们，或分配给他们一个惩罚的适应性。然而，这会导致有益信息的丢失，或者导致模型具有严重变形的景观。为了解决这些问题，本研究提出了一个混合分类器模型框架。分类器的作用是预测哪些候选解决方案可能会使模拟崩溃，然后使用此预测将搜索偏向有效解决方案。此外，所提出的框架采用了信任区域的方法，和其他几个程序，来管理模型和分类器，并确保优化的进展。使用翼型形状优化的工程应用程序的性能分析表明，所提出的框架的有效性，以及使用分类器中积累的知识来获得新的见解正在解决的问题的可能性。
Many real-world optimisation problems rely on computationally expensive simulations to evaluate candidate solutions. Often, such problems will contain candidate solutions for which the simulation fails, for example, due to limitations of the simulation. Such candidate solutions can hinder the effectiveness of the optimisation since they may consume a large portion of the optimisation budget without providing new information to the optimiser, leading to search stagnation and a poor final result. Existing approaches to handle such designs either discard them altogether, or assign them a penalised fitness. However, this results in loss of beneficial information, or in a model with a severely deformed landscape. To address these issues, this study proposes a hybrid classifier-model framework. The role of the classifier is to predict which candidate solutions are likely to crash the simulation, and this prediction is then used to bias the search towards valid solutions. Furthermore, the proposed framework employs a trust-region approach, and several other procedures, to manage the model and classifier, and to ensure the progress of the optimisation. Performance analysis using an engineering application of airfoil shape optimisation shows the efficacy of the proposed framework, and the possibility to use the knowledge accumulated in the classifier to gain new insights into the problem being solved.