Wang Zhijun, Wang Qing
J Comput Sci Syst Biol. 2015;9(1):1-5. doi: 10.4172/jcsb.1000213.
Tumor cell growth models involve high-dimensional parameter spaces that require computationally tractable methods to solve. To address a proposed tumor growth dynamics mathematical model, an instance of the particle swarm optimization method was implemented to speed up the search process in the multi-dimensional parameter space to find optimal parameter values that fit experimental data from mice cancel cells. The fitness function, which measures the difference between calculated results and experimental data, was minimized in the numerical simulation process. The results and search efficiency of the particle swarm optimization method were compared to those from other evolutional methods such as genetic algorithms.
肿瘤细胞生长模型涉及高维参数空间，这需要计算上易于处理的方法来求解。为了解决一个提出的肿瘤生长动力学数学模型，实施了粒子群优化方法的一个实例，以加速在多维参数空间中的搜索过程，从而找到适合小鼠癌细胞实验数据的最优参数值。在数值模拟过程中，使测量计算结果与实验数据之间差异的适应度函数最小化。将粒子群优化方法的结果和搜索效率与其他进化方法（如遗传算法）的结果和搜索效率进行了比较。
