Abstract
In this paper, we propose a novel solution strategy to explicitly
describe the design space in which no recourse is considered for the
realization of the parameters. First, to smooth the boundary of the
design space, the Kreisselmeier-Steinhauser (KS) function is applied to
aggregate all inequality constraints, and project them into the design
space. Next, for creating a surrogate polynomial model of the KS
function, we focus on finding the sampling points on the boundary of KS
space. After testing the feasibility of Latin hypercube sampling points,
two methods are presented to efficiently extend the set of boundary
points. Finally, a symbolic computation method, cylindrical algebraic
decomposition, is applied to transform the surrogate model into a series
of explicit and triangular subsystems that can be further converted to
describe the KS space. Two case studies are considered to show the
efficiency of the proposed algorithm.