Machine-Learning in Simulation-Driven Optimization

Abstract

Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an unknown reason, which in turn hampers the optimization process. To handle such scenarios more effectively this study proposes the integration of classifiers, borrowed from the domain of machine learning, into the optimization process. Numerical experiments show that the proposed approach improves the effectiveness of the optimization search.