Comparison of Constraint-handling Methods for the Sequential Approxi- mate Optimization of Functionally Graded Plates

Resumo

The optimal material design in Functionally Graded (FG) structures can be defined by an optimization
procedure. This is often performed by the use of bio-inspired algorithms, even though they may require thousands
of function evaluations. Alternatively, a surrogate model can be used to provide a faster assessment of the structural
response. In this work, the Sequential Approximate Optimization (SAO) will be employed, where the approximate

surface will be iteratively improved by the addition of new points in regions of interest. When constraint func-
tions need to be approximated by a surrogate model, a feasibility function can be considered to account for the

uncertainty in determining the design’s feasibility. The SAO approach will be employed in the optimization of
Functionally Graded Plates considering expensive constraints, and different feasibility functions will be tried out.
The optimization will also be carried out using a bio-inspired algorithm, and these approaches will be compared in
terms of efficiency and accuracy.