This paper addresses the critical issue of fidelity in simulation-based design optimization using preference-based surrogate models. Specifically, it presents an integrated clustering-based updating procedure in a genetic algorithm setup to iteratively improve the efficacy of Kriging models. A potential drawback of using preference-based surrogate models in simulation based design is that the surrogates may misrepresent the true optima if the model building schemes fail to capture the critical points of interest with enough fidelity or clarity. This work addresses this vulnerability and presents an efficient clustering-technique integrated surrogate model updating procedure that can capture the buried, transient, yet inherent data pattern in the evolution progression of design candidates within a genetic algorithm setup, and screen out distinct optimal points for subsequent sequential model validation and updating. The results show that the successful finding of the true optimal design through cost-effective surrogate-based optimization depends not only on the selection of sampling schemes such as sample rate and distribution in the initial surrogate model build-up, but also on an efficient and reliable updating procedure that can prevent suboptimal decisions.

This content is only available via PDF.
You do not currently have access to this content.