An artificial intelligence and numerical simulation-based strategy for multi-objective optimization of hemodynamics in curved vessels.

Understanding and optimizing blood flow behavior in curved vessels is crucial for improving cardiovascular treatment planning and reducing complications such as thrombosis and impaired oxygen transport. Although recent advances in computational modeling have enhanced the simulation of blood rheology, systematic optimization of flow parameters under controlled geometric conditions remains limited. This study addresses this gap by optimizing the hemodynamic performance of curved blood vessels using numerical modeling and multi-objective optimization. Three key parameters hematocrit level, kinematic viscosity, and curvature degree were analyzed to enhance flow velocity and minimize shear stress, balancing oxygen delivery and thrombus formation risks. A numerical model based on the HemoCell framework and the Lattice Boltzmann-Immersed Boundary Method simulated 250 scenarios across ten vessel geometries with varying curvature. Predictive modeling was performed using gradient boosting and support vector regression integrated with bio-inspired optimization algorithms. The Sparrow Search Algorithm combined with gradient boosting achieved the highest velocity prediction accuracy (R2 = 0.99902), while the Hunger Game Search Algorithm demonstrated superior performance in shear stress prediction (R2 = 0.99577). These results confirmed the reliability of the surrogate models for optimization. Pareto fronts and optimal points were then identified, revealing a shear stress of 587.70 Pa and a velocity of 0.039 m/s as one of the optimal trade-offs. The proposed approach provides a validated framework for hemodynamic optimization and provides preliminary insights that could inform future studies on thrombus risk assessment and the bio-inspired design of bypass grafts.