Institute of Technology, Resource and Energy-efficient Engineering (TREE), Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, 53757, Germany
Institute of Technology, Resource and Energy-efficient Engineering (TREE), Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, 53757, Germany.
Evol Comput. 2023 Sep 1;31(3):287-307. doi: 10.1162/evco_a_00326.
Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing hundreds of thousands of evaluations. Even with the assistance of surrogate models, quality diversity needs hundreds or even thousands of evaluations, which can make its use infeasible. In this study, we try to tackle this problem by using a pre-optimization strategy on a lower-dimensional optimization problem and then map the solutions to a higher-dimensional case. For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints. For a diverse set of building designs, by sampling the space of 2D footprints with a quality diversity algorithm, a predictive model can be trained that is more accurate than when trained on a set of footprints that were selected with a space-filling algorithm like the Sobol sequence. Simulating only 16 buildings in 3D, a set of 1,024 building designs with low predicted wind nuisance is created. We show that we can produce better machine learning models by producing training data with quality diversity instead of using common sampling techniques. The method can bootstrap generative design in a computationally expensive 3D domain and allow engineers to sweep the design space, understanding wind nuisance in early design phases.
质量多样性算法可用于有效地创建一组多样化的解决方案，以丰富工程师的直觉。但在非常昂贵的问题中，质量多样性并不高效，需要数十万次评估。即使有代理模型的帮助，质量多样性也需要数百甚至数千次评估，这使得其使用变得不可行。在这项研究中，我们试图通过在低维优化问题上使用预优化策略，然后将解决方案映射到高维问题来解决这个问题。在一个设计 minimizes 风扰的建筑物的用例中，我们展示了我们可以从建筑物轮廓的二维流特征预测三维建筑物周围的流特征。对于一组多样化的建筑物设计，通过使用质量多样性算法对二维轮廓进行抽样，可以训练出一个比使用 Sobol 序列等空间填充算法选择的一组轮廓训练出的预测模型更准确的模型。仅在 3D 中模拟 16 个建筑物，就可以创建一组具有低预测风扰的 1024 个建筑物设计。我们表明，通过使用质量多样性生成训练数据而不是使用常见的抽样技术，我们可以生成更好的机器学习模型。该方法可以在计算成本高昂的 3D 领域中为生成式设计提供支持，并允许工程师在早期设计阶段全面研究设计空间，了解风扰问题。
