Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, 53757, Germany
Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, 53757, Germany
Evol Comput. 2018 Fall;26(3):381-410. doi: 10.1162/evco_a_00231. Epub 2018 Jun 8.
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms currently require a large number of function evaluations, limiting their applicability. In this article, we introduce a new illumination algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate modeling techniques to create a map of the design space according to user-defined features while minimizing the number of fitness evaluations. On a two-dimensional airfoil optimization problem, SAIL produces hundreds of diverse but high-performing designs with several orders of magnitude fewer evaluations than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of producing maps of high-performing designs in realistic three-dimensional aerodynamic tasks with an accurate flow simulation. Data-efficient design exploration with SAIL can help designers understand what is possible, beyond what is optimal, by considering more than pure objective-based optimization.
设计优化技术通常在设计过程的开始阶段使用，以探索可能的设计空间。在这些领域中，照明算法（如 MAP-Elites）是经典优化算法的有前途的替代品，因为它们可以在单次运行中生成多样化的高质量解决方案，而不仅仅是一个接近最优的解决方案。不幸的是，这些算法目前需要大量的函数评估，限制了它们的适用性。在本文中，我们引入了一种新的照明算法，即代理辅助照明（SAIL），它利用代理建模技术根据用户定义的特征创建设计空间的地图，同时最小化适应度评估的数量。在二维翼型优化问题上，SAIL 生成了数百个不同但高性能的设计，与 MAP-Elites 或 CMA-ES 相比，评估数量减少了几个数量级。我们证明，SAIL 还能够在具有准确流模拟的现实三维空气动力学任务中生成高性能设计的地图。通过 SAIL 进行高效的数据设计探索可以帮助设计师理解什么是可能的，而不仅仅是基于最优的考虑。
