Clustering-enhanced Bayesian optimization (CEBO): A case study on T-pedal wheel design of a stair-climbing robot

This study presents a clustering-enhanced Bayesian optimization (CEBO) framework that improves the efficiency and stability of Bayesian optimization (BO) by incorporating a plausible region search (PRS) algorithm. Using prediction values from the surrogate model, the PRS algorithm identifies regions likely to contain the global optimum through candidate screening, clustering, and plausible region identification. CEBO reduces the search space by filtering out samples with low prediction values and identifying informative clusters via K-means clustering combined with silhouette analysis. Leveraging multiple prediction values, the cluster with the lowest mean prediction is designated as the plausible region, within which the next evaluation point is selected using the expected improvement (EI) criterion to efficiently update the surrogate model. Extensive numerical validation on benchmark problems ranging from 2D to 12D demonstrates that CEBO consistently outperforms conventional BO methods by reducing the number of function evaluations. In a 6D T-pedal wheel design problem, CEBO further demonstrates its practical applicability by achieving the lowest standard deviation while reducing the number of function evaluations, thereby confirming both superior efficiency and stability beyond synthetic benchmarks.