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Parameter estimation for a mechanistic model of high dose irradiation damage using Nelder-Mead simplex method and genetic algorithm

机译:基于Nelder-Mead单纯形法和遗传算法的高剂量辐照损伤机理模型参数估计

摘要

Radiation therapy is one of the cancer cells treatments that use high-energy radiation to shrink tumors and kill cancer cells. Radiation therapy kills cancer cells by damaging their DNA directly or creates charged particles within the cells that can in turn damage the DNA. As a side effect of the treatment, the radiation therapy can also damage the normal cell that located at parts of our body. The main goals of radiation therapy are to maximize the damaging of tumors cell and minimize the damage of normal tissue cell. Hence, in this study, we adopt an existing model of high dose irradiation damage. The purpose of this study is to estimate the six parameters of the model which are involved. Two optimization algorithms are used in order to estimate the parameters: Nelder-Mead (NM) simplex method and Genetic Algorithm (GA). Both methods have to achieve the objective function which is to minimize the sum of square error (SSE) between the experimental data and the simulation data. The performances of both algorithms are compared based on the computational time, number of iteration and value of sum of square error. The optimization process is carried out using MATLAB programming built-in functions. The parameters estimation results shown that Nelder-Mead simplex method is more superior compare to Genetic Algorithm for this problem.
机译:放射疗法是使用高能放射线缩小肿瘤并杀死癌细胞的癌细胞治疗方法之一。放射疗法通过直接破坏癌细胞的DNA杀死癌细胞或在细胞内产生带电荷的粒子,进而带动DNA的破坏。作为治疗的副作用,放射疗法还会损害位于我们身体各个部位的正常细胞。放射疗法的主要目标是使肿瘤细胞的损害最大化,并使正常组织细胞的损害最小化。因此,在这项研究中,我们采用了现有的高剂量辐射损伤模型。这项研究的目的是估计模型的六个参数。为了估计参数,使用了两种优化算法:Nelder-Mead(NM)单纯形法和遗传算法(GA)。两种方法都必须达到目标功能,即使实验数据和仿真数据之间的平方误差之和(SSE)最小。根据计算时间,迭代次数和平方误差和的值比较两种算法的性能。优化过程是使用MATLAB编程内置函数执行的。参数估计结果表明,与遗传算法相比,Nelder-Mead单纯形法在此问题上更具优势。

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