用微进化算法反演岩石蠕变模型非定常参数

Microevolution Algorithm for Inversion of Non-stationary Parameters in Rock Creep Model

岩石蠕变模型的参数较多,为得到参数的全局最优解,应用微进化算法(M icroevolution A lgorithm,MA)对岩石蠕变模型非定常参数进行了反演分析。算法以实测蠕变值与理论计算值之间的最小二乘误差为优化准则函数,直接反演计算蠕变模型参数。计算结果表明,微进化算法可最大限度地利用所有试验数据,避免传统优化算法初始参数选取的困难,且算法简单有效,计算精度高于混沌粒子群优化算法。该方法也可推广应用于其它蠕变模型的参数反演,具有较高的工程应用价值。

Rock creep model generally contains several parameters.To obtain the global optimal solution of the parameters,Microevolution Algorithm（MA） was employed for the inversion of non-stationary parameters.In this paper,parameters of the creep model are directly inversed with the least square error between measured creep values and calculated creep values as the optimization criterion function.The computation results demonstrate that microevolution algorithm can maximize the use of all test data and avoid the difficulty of selecting initial parameters in traditional optimization algorithm.Moreover,microevolution algorithm is simple and effective,and offers higher accuracy than Chaos Particle Swarm Optimization（CPSO）.In this sense,it can be applied to the inversion of parameters in other creep models and has high application value for engineering.