Hybrid Particle swarm optimization (HPSO) algorithm is a stochastic global optimization technique with many advantages, such as quick convergence, simple regulation and easy implementation. In order to determine the time-varying parameters of creep constitutive model of rock, in this article, a new method is presented using HPSO algorithm and fish language, which was contained in FLAG At first, the stochastic values of parameters are initialized and the difference between the value computed and the datum measured during creep was regarded as fitness function to evaluate quality of the parameters. Then the parameters are updated continually using HPSO until the optimal parameters are found. Thus time-varying parameters of creep constitutive model of rock are identified adaptively during computation. Simulations was done for shale creep experiment, the results show that hybrid particle swarm optimization algorithm is effective in identifying the time-varying parameters of creep constitutive model of rock and viscoelastic characteristics of shale can be described better by using inconstant creep ? Constitutive model.%复合微粒群优化(HPSO)是一类随机全局优化技术,具有搜索能力强、收敛速度快、搜索精度高的优点.针对岩石蠕变本构模型非定常参数的辨识问题,利用FLAC软件自带的fish语言实现了HPSO算法对非定常参数的辨识.该方法从非定常参数的随机值出发,以蠕变过程中试件变形的实验值与计算值的误差大小作为适应度函数来评价参数的品质,利用HPSO算法规则实现非定常参数的进化,搜索出全局最优的模型参数值,从而实现了岩石蠕变本构模型非定常参数的自适应辨识.利用该方法对页岩蠕变实验进行了仿真研究,与文[9]的结果对比发现:HPSO算法用于岩石蠕变模型的非定常参数辨识是有效的,非定常参数的本构模型能更好的描述页岩的粘弹性变形性能.
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