阶梯状黄土边坡稳定性分析的改进遗传算法及应用

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.

An Improved Genetic Algorithm and Application of the Stability Analysis for Ladder-Like Loess Slope

The key of analyzing the ladder-like loess slope stability is to calculating its minimum stability coefficient.The stabihty coefficient involves many factors,and its computation process is more complicated,so classical optimization algorithms will not achieve the global minimum solution effectively.In view of this,we propose an improved method of adaptive Genetic Algorithm(GA).This algorithm makes the gene variable space generate the grid,and uses the iterative selection methods to build the initial population with uniform distribution.Moreover,the algorithm employs the elitism strategy and adaptively adjusts the crossover and mutation probability according to the given rules,as well as improves the global searching capability and convergence rate of GA.The apphcation instance indicates that the algorithm can converge to the global minimum solution of ladder-like slope stability coefficient more quickly and efficiently,and its computation results are exactly consistent with actual facts.