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.