Accelerating sampling-based tolerance-cost optimization by adaptive surrogate models

High numbers of function evaluations are inevitable to guarantee the reliability and optimality of sampling-based tolerance-cost optimization. Despite using different countermeasures to increase its efficiency, unreasonably long computation times and unreliable results currently hinder its profitable application. Motivated to overcome this shortcoming, this article presents a novel strategy harmonizing metaheuristic optimization and surrogate modelling. It is based on the idea of adaptive surrogate model-based optimization substituting the tolerance analysis subroutine with a surrogate model, which is iteratively re-modelled with intermediate optimization results to improve its accuracy continuously in potential optima regions. On the one hand, a directed intensification of promising solutions and, on the other hand, an accelerated exploration of the search space is achieved. Optimization studies prove its positive effect on overall efficiency, where a case study with multiple nonlinear assembly response functions and geometrical tolerances serves as a use case.