Many objective visual analytics: rethinking the design of complex engineered systems

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.

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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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Correspondence to Patrick M. Reed.

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Woodruff, M.J., Reed, P.M. & Simpson, T.W. Many objective visual analytics: rethinking the design of complex engineered systems. Struct Multidisc Optim 48, 201–219 (2013). https://doi.org/10.1007/s00158-013-0891-z

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Keywords

  • Multi-objective optimization
  • Multidimensional data visualization
  • Product family design