Abstract
Engineering design processes often use optimization strategies, which aim to minimize multi-objective functions. The analysis should consider the uncertainty in a system, which may cause significant changes in its behavior. The inclusion of the uncertainty in the design process makes the identification of an optimum design more challenging. In this paper, two novel optimization methods (Iterative Distribution Evolutionary Algorithm [I.D.E.A.] and Reliable & Robust Evolutionary Algorithm [R.R.E.A.]) are presented. These optimization strategies aim to solve problems that are very time-demanding and for which it is difficult (and expensive) to determine derivatives and to identify and define the optimum set of parameters. The approaches are validated considering as a test case the optimization of a landing gear system in order to avoid the onset of shimmy, assuring a reliable design.
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