基于加速混合遗传算法的非线性地表沉降模型参数优化研究  

OPTIMIZING PARAMETERS OF NONLINEAR GROUND SUBSIDENCE MODEL BASED ON ACCELERATED HYBRID GENERIC ALGORITHM

作　　者：张永兴[1] 陈秋南[1] 任伯帜[2] 

机构地区：[1]重庆大学土木工程学院,重庆400045 [2]湖南科技大学土木工程学院,湖南湘潭411201

出　　处：《工程力学》2005年第4期43-47,共5页Engineering Mechanics

基　　金：国家"十五"科技攻关资助项目(2001BA604A02);湖南省教育厅资助项目(03C509)

摘　　要：基于随机介质理论建立非线性地表沉降模型,将DFP变尺度算法嵌入到改进浮点编码遗传算法中,经加速循环得到加速混合遗传算法,对沉降模型参数进行优化分析。实例显示加速混合遗传算法优化地表沉降模型的三个参数的精度高于其它方法。The ground surface subsidence of underground cave is serious geological hazard. In order to predict ground surface subsidence of underground cave a nonlinear subsidence model of underground cave is established according to stochastic medium method. An accelerated hybrid generic algorithm is put forward based on the DFP method and modified float genetic algorithm, and applied to optimizing parameters of nonlinear subsidence model Results show that the accelerated hybrid generic algorithm is practical, efficient and superior to other methods.

关 键 词：隧道工程 非线性地表沉降模型 混合遗传算法 参数优化 随机介质理论 

分 类 号：TB114.3[理学—概率论与数理统计]