改进的遗传算法在生物组织热特性参数无损测量中的应用
Application of improved genetic algorithm to the noninvasive measurement of thermal parameters for living tissues
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摘要: 针对生物活体组织的多个热特性参数同时测量的难点问题,提出了采用遗传算法同时估计多个活体组织热特性参数的方法,设计了实数编码的遗传算法.通过对选择、交叉和突变算子进行改进,并引入小生境策略,提高了遗传算法的全局寻优能力和搜索效率.对动态体模和人体前臂的热特性参数测量的模拟仿真研究和实验研究表明,采用改进的遗传算法,能够以较高的精度同时估计生物活体组织的多个热特性参数.Abstract: The simultaneous measurement of multiple thermal parameters of living tissues is of great significance for medical clinical applications. A parameter estimation method using improved genetic algorithm (GA) was proposed to simultaneously estimate the multiple thermal parameters of living tissues. In the method the real-coded GA was designed, the selection, crossover and mutation operators were improved, and the niche mechanism was applied to improve the capability of global optimization. The simulation and experimental researches of a dynamic phantom and a human forearm indicate that it is feasible and effective to simultaneously estimate the multiple thermal parameters of living tissues with high accuracy by the proposed method.