Many objective visual analytics: rethinking the design of complex engineered systems
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Abstract
Many cognitive and computational challenges accompany the design of complex engineered systems. This study proposes the many-objective visual analytics (MOVA) framework as a new approach to the design of complex engineered systems. MOVA emphasizes learning through problem reformulation, enabled by visual analytics and many-objective search. This study demonstrates insights gained by evolving the formulation of a General Aviation Aircraft (GAA) product family design problem. This problem’s considerable complexity and difficulty, along with a history encompassing several formulations, make it well-suited to demonstrate the MOVA framework. The MOVA framework results compare a single objective, a two objective, and a ten objective formulation for optimizing the GAA product family. Highly interactive visual analytics are exploited to demonstrate how decision biases can arise for lower dimensional, highly aggregated problem formulations.
Keywords
Multi-objective optimization Multidimensional data visualization Product family designNotes
Acknowledgments
The second author of this work was partially supported by the US National Science Foundation under Grant CBET-0640443. The computational resources for this work were provided in part through instrumentation funded by the National Science Foundation through Grant OCI-0821527. Any opinions,findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation.
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