Affiliation:
1. Massachusetts Institute of Technology
2. Chinese University of Hong Kong
3. University of Washington
Abstract
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. The objectives that define this 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.
Funder
Intelligence Advanced Research Projects Agency
NSF
NSF GRFP
Defense Advanced Research Projects Agency
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design
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