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阶梯状黄土边坡稳定性分析的关键是估算其稳定系数的最小值.稳定系数的求解涉及诸多因素且计算过程繁杂,传统优化算法往往不能有效地搜索到其全局最小解.为此,提出一种改进的自适应遗传算法.算法对基因变量空间进行网格状划分,采用迭代选优法建立均匀分布的初始种群,运用优质个体保留遗传策略,并按照特定的准则自适应地调整交叉概率和变异概率,提高算法的全局搜索能力和收敛速度.实例应用表明算法能够快速有效地收敛于土坡稳定系数的全局最小解,且计算结果与实际情况更加吻合.
The key to the stability analysis of stepped loess slopes is to estimate the minimum value of its stability coefficient.The solution of stability coefficient involves many factors and the calculation process is complicated, and the traditional optimization algorithm can not search the global minimum solution efficiently.Therefore, An improved adaptive genetic algorithm is proposed.The algorithm divides the space of genetic variables into grids and uses an iterative optimization method to establish an evenly distributed initial population and uses high quality individuals to retain the genetic strategy and adaptively adjusts the crossover probability and Mutation probability, global search ability and convergence speed of the algorithm.Examples show that the algorithm can converge quickly and effectively to the global minimum solution of soil slope stability coefficient, and the calculation results are more in line with the actual situation.