Abstract
AbstractThis paper describes an approach for optimizing multibody systems involving spatial tracks, addressing the problem of finding an optimal set of design parameters when no good initial guess is available. In this setting, it is common that, due to nonlinearities of the objective function and singularities in the kinematic model, the optimization fails to converge or to find feasible points at all. The present paper presents a three‐stage procedure for sequentially guiding a standard optimization routine from an arbitrary initial guess to the optimal configuration. The approach has been tested when optimizing real roller coaster tracks with respect to passenger acceleration and compared with a genetic algorithm implementation, showing that objective function morphing renders faster for the regarded type of systems. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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