浮点遗传算法的收敛性及其在模型参数提取问题中的应用

浮点遗传算法是一种模拟生物进化的最化搜索法,由于其运算简单、稳定性好、不需要计算目标函数的导数、高精度和能处理多维数值问题,浮点遗传算法在科学研究和工程技术中得到了广泛应用.通过对浮点遗传算法收敛性的分析,本文证明了"简单浮点遗传算法不收敛于全局最优解,而每代保留最优个体的浮点遗传算法才收敛于全局最优解".在此基础上,本文设计了一种采用连续突变和每代保留最优个体的改进浮点遗传算法,它克服了精确度与计算量之间的矛盾.本文利用该算法较好地解决了半导体器件模型参数提取问题,使计算量降低了约27%.

The Convergence of Floating Genetic Algorithms and Its Application in Model Parameter Extraction

Floating genetic algorithms (FGAs) are optimization methods simulating the natural evolution mechanism.FGAs have been widely used in science and technology by virtue of their simplicity,robustness,freedom of calculating the gradient of the objective function,high precision and the ability of solving multi dimensional numerical problems.With the convergence analysis of FGAs,it is proved in this paper that FGAs with the fittest individual holding in each generation can converge to the global optimum while simple FGAs can not.In the light of the theoretical convergence analysis,improved FGAs with the fittest individual holding and the continuous mutation are proposed,which overcome the incompatibility between the high precision and low computational cost.The improved FGAs have been applied to extracting the semiconductor device model parameters,and have gained about 27% reduction to the computational cost.