Abstract: BACKGROUND: Percutaneous coronary intervention stent implantation is primarily used to treat coronary artery stenosis. However, current multi-objective stent optimization methods are limited by sample size constraints, resulting in insufficient prediction accuracy when balancing key performance indicators such as support and compliance, hindering the effectiveness of stent optimization design.
OBJECTIVE: To establish an innovative optimization framework for coronary stents based on a ensemble surrogate model.
METHODS: A three-dimensional parametric model of the vascular stent was constructed, and a mechanical response database was established through finite element simulation. A dynamic weight fusion strategy was adopted to integrate the global optimization characteristics of the Kriging model and the local nonlinear representation advantages of the radial basis function model. A ensemble surrogate model was constructed based on 20 groups of initial samples, and the non-dominated sorting genetic algorithm-II was used to optimize the parameter space.
RESULTS AND CONCLUSION: Experimental results demonstrated that the ensemble surrogate model exhibited significant advantages in the finite sample setting. The coefficient of determination for the inverse prediction of the radial stiffness of the stent reached 0.974 2, a 4.4% improvement compared to the single model, validating the efficient modeling capability of the ensemble surrogate model in the finite sample setting. The prediction accuracy of the stent's bending stiffness also improved by 4.4% compared to the single radial basis function surrogate model. After optimization, the stent performance achieved dual-objective collaborative optimization. The inverse radial stiffness of the stent in the ensemble surrogate model group was reduced by 13.92% and 9.57% compared to the Kriging model group and the single radial basis function model surrogate group, respectively. The stent's bending stiffness was improved by 0.38% and 2.56% compared to the Kriging model group and the single radial basis function model surrogate group, respectively. The proposed ensemble surrogate model overcomes the performance limitations of traditional single models and provides a low-cost, high-precision solution for the "rigidity-flexibility" collaborative optimization of coronary stents. 

Key words: coronary stent, ensemble surrogate mode, multi-objective optimization, finite element analysis, biomechanics, optimization method, radial stiffness, bending stiffness, Kriging model, radial basis function