Pareto gamuts: exploring optimal designs across varying contexts
Pareto gamuts: exploring optimal designs across varying contexts
复制标题
帕累托域：探索不同环境下的最佳设计
DOI：
10.1145/3450626.3459750
复制
发表时间：
2021
影响因子：
6.2
通讯作者：
Matusik, Wojciech
中科院分区：
文献类型：
--
作者：
Makatura, Liane;Guo, Minghao;Schulz, Adriana;Solomon, Justin;Matusik, Wojciech
制造的零件经过精心设计，在重量、压力和成本等几个相互冲突的指标上表现良好。可实现的最佳权衡位于帕累托前沿，这可以通过性能驱动的优化来发现。用于定义帕累托正面的目标函数通常包含关于零件将在其中使用的环境的假设，包括载荷条件、环境影响、材料特性或必须保留以与周围组件连接的区域。现有的多目标优化工具一次只能研究一个环境，因此工程师必须为每个感兴趣的环境运行独立的优化。然而，工程零件经常出现在许多情况下：风力涡轮机必须在多种风速下表现良好，支架可能会多次优化，将其螺栓孔固定在每次运行的不同位置。本文提出了一种变背景多目标优化的框架。我们介绍了帕累托色域，它捕捉了一系列背景下的帕累托前沿。我们开发了一种全局-局部优化算法来直接发现Pareto色域，而不是一次发现一个固定上下文的“切片”。为了验证我们的方法，我们将现有的多目标优化基准应用到上下文场景中。我们还展示了帕累托色域探索法在几个工程设计问题上的实用价值。
Manufactured parts are meticulously engineered to perform well with respect to several conflicting metrics, like weight, stress, and cost. The best achievable trade-offs reside on the Pareto front, which can be discovered via performance-driven optimization. Objective functions used to define the Pareto front often incorporate assumptions about the context in which a part will be used, including loading conditions, environmental influences, material properties, or regions that must be preserved to interface with a surrounding assembly. Existing multi-objective optimization tools are only equipped to study one context at a time, so engineers must run independent optimizations for each context of interest. However, engineered parts frequently appear in many contexts: wind turbines must perform well in many wind speeds, and a bracket might be optimized several times with its bolt-holes fixed in different locations on each run. In this paper, we formulate a framework for variable-context multi-objective optimization. We introduce the Pareto gamut, which captures Pareto fronts over a range of contexts. We develop a global-local optimization algorithm to discover the Pareto gamut directly, rather than discovering a single fixed-context "slice" at a time. To validate our method, we adapt existing multi-objective optimization benchmarks to contextual scenarios. We also demonstrate the practical utility of Pareto gamut exploration for several engineering design problems.