基于改进狼群算法的概率积分法模型参数反演方法

Parameter inversion method of probability integral model based on improved wolves algorithm

概率积分法是国内广泛应用的开采沉陷预计方法,如何基于实测数据,精确、快速、可靠地获得概率积分法模型参数,一直是该方法应用的难点。鉴于此,在分析WPA优缺点的基础上提出二次游走、变异行为改进策略,形成了改进狼群算法(IWPA),并将IWPA引入概率积分法模型参数反演中,构建基于改进狼群算法的概率积分法模型参数反演方法(MIWPA)。模拟试验结果表明:MIWPA反演参数相对误差、参数中误差分别不超过3.4%,4.02,且MIWPA的准确性、可靠性均优于MWPA;将MIWPA应用在淮南矿区顾桥矿1414(1)工作面的概率积分法模型参数反演中,获取的概率积分法模型参数为q=0.93,tanβ=1.98,b=0.42,θ=84.53°,S1=-12.44 m,S2=-18.80 m,S3=55.06 m,S4=33.98 m,下沉值与水平移动值拟合中误差为114.88 mm,满足工程应用要求。

Probabilistic integral method is a widely used method for predicting mining subsidence in China.How to accurately,quickly and reliably obtain the probability integral method model parameters based on the measured data has always been the difficulty of this method.Given this,based on the analysis of the advantages and disadvantages of WPA,this paper proposes the improvement strategy of second-migration mutation behavior,forms the improved wolves algorithm(IWPA),and introduces the IWPA into the probabilistic integral method for predicting parameters inversion,and constructs the probabilistic integral method model parameter inversion method(MIWPA)based on the improved wolves algorithm.The simulation results show that the relative error and median error of MIWPA inversion parameters are less than 3.4% and 4.02 respectively,and the accuracy and reliability of MIWPA are better than that of MWPA.Applying MIWPA to the parameter inversion of the probability integral method model of 1414(1)working face in Guqiao Mine of Huainan Mining Area,and the obtained probability integral model parameters were q=0.93,tanβ=1.98,b=0.42,θ=84.53,S1=-12.44 m,S2=-18.80 m,S3=55.06 m,S4=33.98 m.The median error of subsidence and horizontal movement fitting was 114.88 mm,which met the engineering application accuracy.