Using Modeling Knowledge to Guide Design Space Search

Andrew Gelsey, Mark Schwabacher and Don Smith

January 1996

Automated search of a space of candidate designs seems an attractive
way to improve the traditional engineering design process. To make
this approach work, however, the automated design system must include
both knowledge of the modeling limitations of the method used to
evaluate candidate designs and also an effective way to use this
knowledge to influence the search process. We suggest that a
productive approach is to include this knowledge by implementing a set
of model constraint functions which measure how much each modeling
assumptions is violated, and to influence the search by using the
values of these model constraint functions as constraint inputs to a
standard constrained nonlinear optimization numerical method. We test
this idea in the domain of conceptual design of supersonic transport
aircraft, and our experiments indicate that our model constraint
communication strategy can decrease the cost of design space search by
one or more orders of magnitude.