Engineering-Scale B-Spline Surface Reconstruction Using a Hungry Predation Algorithm, with Validation on Ship Hulls

This paper tackles a core challenge in reverse engineering: high-fidelity reconstruction of continuous B-spline surfaces from discrete point clouds, where optimal knot placement remains pivotal yet not fully resolved. We propose a new fitting method based on the Hungry Predation Algorithm (HPA) to improve efficiency, accuracy, and robustness. This method introduces a hybrid knot-guidance strategy that combines geometry-aware preselection with a complexity-driven probabilistic distribution to address knot placement. On the optimization side, HPA simulates starvation-driven predator–prey dynamics to enhance global search capability, maintain population diversity, and accelerate convergence. We also develop an adaptive parameter adjustment framework that automatically tunes key settings according to surface complexity and accuracy thresholds. Comparative experiments against classical approaches, six state-of-the-art optimizers, and the commercial CAD system CATIA demonstrate HPA’s superiority in control-point reduction, fitting accuracy, and computational efficiency. This method shows high applicability to engineering-scale tasks (e.g., ship hull design), where the point-to-surface RMSE (e.g., <10−3 Lmax) achieved satisfies stringent requirements for downstream hydrodynamic performance analysis and manufacturing.