Optimization and complexity analysis of homogeneous coagulation models

This study evaluates the performance of optimization strategies across homogeneous coagulation models of varying complexity, distinguished by differences in the number of species and reactions. In the two extrinsic pathway models examined, the more detailed scheme exhibits a more complex optimization landscape, with numerous local minima and increased convergence difficulties compared with the reduced model. To address these issues, we propose a hybrid optimization framework integrating gradient-based methods with evolutionary algorithms. When applied to synthetic numerical datasets, the approach demonstrates robust and reliable convergence. The strategy is further validated using real clinical and experimental thrombin generation data, confirming its practical utility in modeling physiological conditions and guiding treatment decisions.