Metamodel-based Multi-objective Robust Design
Optimization Of Structures
Author(s)
J. Martinez-Frutos & P. Marti-Montrull
Abstract
Multi-objective robust optimization (MORO) is a highly demanding computational
task because of the direct nesting of the uncertainty quantification within
optimization. This work presents an approach based on Kriging models to
efficiently include the uncertainty quantification in the optimization procedures.
In the proposed approach the metamodels appear both at optimization level as
well as at uncertainty quantification level. The proposed methodology allows us
to: (1) assess the robustness of each design using a reduced number of simulator
runs, as compared with conventional approach procedures; and (2) to decouple the
uncertainty quantification of the optimization, allowing us to solve the problem
with a lower computational cost compared to the nested approach. A benchmark
problem has been considered using different approaches in order to compare their
relative merits. The results show that the proposed method has potential to obtain
solutions with reasonable accuracy and a considerably lower number of function
calls than required by conventional methods.
Keywords: efficient multi-objective robust optimization, evolutionary algorithms,
Kriging models.
1 Introduction
In structural engineering, the designer often has to deal with problems that involve
conflicting objectives and sources of uncertainty in diverse structural parameters,
such as geometric imperfections, material properties or applied loads. The set
of optimal solutions obtained using conventional deterministic methods can be
very sensitive to perturbations in design variables, leading to a deterioration of the
optimal structural performance or even to a set of infeasible designs. Hence, it is
desirable to obtain a set of optimal solutions which are less sensitive to variations
Keywords
efficient multi-objective robust optimization, evolutionary algorithms,Kriging models.
Related Book
Other papers in this volume
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