求解药代动力学参数的自适应混合粒子群算法

摘要

Traditional method is sensitive to initial values and evolutionary search algorithm can not determine the scope of search on the parameters optimization of pharmacokinetics, the paper combines Particle Swarm Optimization(PSO) with Nelder-Mead Simplex Method(NM-SM),proposes a Adaptive Hybrid PSO(AHPSO) with Time-Varying Acceleration Coefficients(TVAC).The hybrid algorithm is applied to optimize the parameters of two-compartment model with extravascular administration. The simulation results show that AHPSO is a novel and good algorithm with high accuracy and strong robustness for parameters optimization of pharmacokinetics.%针对传统方法具有初始值敏感和进化算法无法确定搜索范围等缺陷,将Nelder-Mead单纯形与粒子群算法相结合,提出了一种基于Neider-Mead单纯形与粒子群算法的具有时变加速因子的自适应混合粒子群算法.将该混合算法用于血管外给药二室模型参数优化的实验之中.仿真实验结果表明,算法计算精度高而且鲁棒性强,是一种新颖的解决药代动力学参数优化的较好方法.