Accelerating sampling-based tolerance-cost optimization
For sampling-based tolerance-cost optimization, a high number of function evaluations is unavoidable, which can lead to long calculation times. To overcome this shortcoming, the latest KTmfk publication presents an innovative strategy that combines metaheuristic optimization and adaptive surrogate modeling. The tolerance analysis is substituted by a surrogate model that is iteratively remodelled with intermediate optimization results. This improves the accuracy of the solutions and speeds up the tolerance-cost optimization process.
Link to the contribution: https://doi.org/10.1080/0305215X.2024.2306142