Background and objective: Mathematical modeling and computational simulations of arterial blood flow network can offer an insilico platform for both diagnostics and therapeutic phases of patients that suffer from cardiac diseases. These models are normally complex and involve many unknown parameters. For physiological relevance, these parameters should be optimized using in-vivo human/animal data sets. The main goal of this work is to develop an efficient, yet an accurate optimization algorithm to compute parameters in the arterial blood flow models.

Methods: The particle swarm optimization (PSO) method is proposed herein for the first time, as an accurate algorithm that applies to computing parameters in the Windkessel type model of blood flow in the arterial system. We begin by defining a 6-element Windkessel (WK6) arterial flow model, which is then implemented and validated using multiple flow rate and aortic pressure measurements obtained from different subjects including dogs, pigs and humans. The parameters in the model are obtained using the PSO technique which minimizes the pressure root mean square (P-RMS) error between the computed and the measured aortic pressure waveform.

Results: Model parameters obtained using the proposed PSO method were able to recover the pressure waveform in the aorta during the cardiac cycle for both healthy and diseased species (animals/humans). The PSO method provides an accurate approach to solve this challenging multi-dimensional parameter identification problem. The results obtained by PSO algorithm was compared with the classical gradient-based, namely the non-linear square fit (NLSF) algorithm.

Conclusions: The results indicate that the PSO method offers alternative and accurate method to find optimal physiological parameters involved in the Windkessel model for the study of arterial blood flow network. The PSO method has performed better than the NLSF approach as depicted from the P-RMS calculations. Finally, we believe that the PSO method offers a great potential and could be used for many other biomedicine optimization problems.

背景与目的：动脉血流网络的数学建模和计算仿真可以为患有心脏病的患者的诊断和治疗阶段提供计算机化平台。这些模型通常很复杂，并且涉及许多未知参数。对于生理相关性，应使用体内人/动物数据集优化这些参数。这项工作的主要目的是开发一种有效而又准确的优化算法来计算动脉血流模型中的参数。

方法：本文首次提出了粒子群优化（PSO）方法，作为一种适用于计算动脉系统血流的Windkessel类型模型中参数的精确算法。我们首先定义一个6要素Windkessel（WK6）动脉血流模型，然后使用从不同对象（包括狗，猪和人）获得的多种流速和主动脉压测量值来实施和验证该模型。使用PSO技术可获得模型中的参数，该技术可最大程度地减少主动脉压力波形与计算值之间的压力均方根（P-RMS）误差。

结果：使用建议的PSO方法获得的模型参数能够恢复健康和患病物种（动物/人类）在心动周期中主动脉的压力波形。PSO方法提供了一种准确的方法来解决这一具有挑战性的多维参数识别问题。将PSO算法获得的结果与基于经典梯度的非线性平方拟合（NLSF）算法进行了比较。

结论：结果表明，PSO方法提供了另一种准确的方法来寻找Windkessel模型中涉及的最佳生理参数，以研究动脉血流网络。如P-RMS计算所示，PSO方法的性能优于NLSF方法。最后，我们认为PSO方法具有巨大的潜力，可用于许多其他生物医学优化问题。