论文部分内容阅读
微循环血管不断地调节自身结构,以适应组织的功能性需求。微循环结构自调节模型能够仿真这一过程,为生理研究提供辅助,但目前缺少合适的模型参数设置方法,限制了模型的进一步应用。本文提出一种改进的量子粒子群优化算法用于设置模型参数,并在真实的大鼠肠系膜微循环血管网络上进行仿真实验。仿真结果表明,该方法的参数优化能力优于标准粒子群算法、标准量子粒子群算法和相关文献报道的Downhill算法,可使微循环结构自调节模型的仿真更接近动物实验数据,并显著提高模型的有效性。
Microcirculation vessels constantly adjust their structure to suit the functional needs of the tissue. The microcirculation self-adjusting model can simulate this process and provide assistance for physiological research. However, there is a lack of suitable method for setting the model parameters, which limits the further application of the model. In this paper, an improved quantum particle swarm optimization algorithm is proposed to set the model parameters and to simulate the real rat mesenteric microcirculation network. The simulation results show that the proposed method has better parameter optimization ability than the standard PSO algorithm. The standard quantum particle swarm optimization algorithm and the Downhill algorithm reported in the literature can make the simulation of the microcirculation self-adjusting model closer to the experimental data of animals and significantly improve the model Effectiveness.