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

Abstract

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.