BACKGROUND Mathematical modeling has achieved a broad interest in the field of biology. These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. However, the estimation of the unknown parameters of the model is a challenging task. Many algorithms have been developed for parameter estimation, but none of them is entirely capable of finding the best solution. The purpose of this paper is to develop a method for precise estimation of parameters of a biological model. METHODS In this paper, a novel particle swarm optimization algorithm based on a decomposition technique is developed. Then, its root mean square error is compared with simple particle swarm optimization, Iterative Unscented Kalman Filter and Simulated Annealing algorithms for two different simulation scenarios and a real data set related to the metabolism of CAD system. RESULTS Our proposed algorithm results in 54.39% and 26.72% average reduction in root mean square error when applied to the simulation and experimental data, respectively. CONCLUSION The results show that the metaheuristic approaches such as the proposed method are very wise choices for finding the solution of nonlinear problems with many unknown parameters.

中文翻译：

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一种用于生物系统参数估计的改进粒子群优化算法。

背景技术数学建模已经在生物学领域中引起广泛兴趣。这些模型用一些数学方程式表示生物现象的新陈代谢之间的关联，以使观察到的生物数据的时程分布符合模型。但是，模型未知参数的估计是一项艰巨的任务。已经开发了许多用于参数估计的算法，但是没有一种算法能够完全找到最佳解决方案。本文的目的是开发一种精确估算生物模型参数的方法。方法本文提出了一种基于分解技术的粒子群优化算法。然后，将其均方根误差与简单粒子群优化算法进行比较，针对两种不同的模拟场景以及与CAD系统代谢相关的真实数据集的迭代无味卡尔曼滤波器和模拟退火算法。结果我们的算法在应用于模拟和实验数据时，均方根误差分别平均降低54.39％和26.72％。结论结果表明，诸如启发式方法之类的元启发法是寻找具有许多未知参数的非线性问题的解决方案的非常明智的选择。