Improved Exploration-Enhanced Gray Wolf Optimizer for a Mechanical Model of Braided Bicomponent Ureteral Stents

Abstract

Ureteral stent tubes are important medical devices used to repair ureteral obstruction or injury. However, relevant experiments of ureteral stent tubes are usually time-consuming and expensive. This research introduces a mechanical model that can simulate the force and deformation of ureteral stents. In addition, a novel optimization algorithm called improved exploration-enhanced gray wolf optimizer (IEE-GWO) is proposed to optimize parameters of the model. In order to balance exploration and exploitation of gray wolf optimizer (GWO), a dimension learning-based hunting (DLH) search strategy and a nonlinear control parameter strategy are integrated into the IEE-GWO. The experimental results show that the proposed IEE-GWO has better performance, such as fast convergence speed and high solution quality. Furthermore, the novel approach can improve the accuracy of the mechanical modal.