Abstract: To overcome the weakness of poor accuracy of traditional methods in terms of optimization of pharmacokinetics parameters, this paper combines the Population Migration Algorithm（PMA） with the Hooke-Jeeves（HJ） algorithm and the order is to make them learn from others’ strong points to offset one’s weaknesses, which not only improves the accuracy of the algorithm, but also speeds up the convergence velocity of the algorithm. Applying the Hybrid Population Migration Algorithm（HPMA） in the experiment on the optimization of two-compartment model’s parameters by extra-vascular administration can achieve a better effect than the traditional method of Feathering method（FM） and higher precision than the HJ or the PMA. Repeated experiments show that this algorithm has strong reliability and stability, and is a good approach for solving pharmacokinetic parameters.

Key words: pharmacokinetics, population migration algorithm, Hooke-Jeeves algorithm, two-compartment model, parameter optimization

摘要： 针对传统方法优化药代动力学参数时精度不高的缺陷，将Hooke-Jeeves算法与人口迁移算法有机融合，使两者取长补短，既提高了算法的精度，又加快了算法的收敛速度。将混合人口迁移算法用于血管外给药二室模型参数优化的实验之中，不仅比传统的残数法效果要好，而且比Hooke-Jeeves算法或人口迁移算法更优，精度更高。多次实验表明：算法具有良好的可靠性和稳定性，是一种较好的解决药代动力学参数的方法。

关键词: 药代动力学, 人口迁移算法, Hooke-Jeeves算法, 二室模型, 参数优化