中文摘要：

针对概率积分法开采沉陷预计参数反演时存在算法复杂、计算量大等问题,将具有算法简单、计算量小、精度高等特点的果蝇算法引入到概率积分法开采沉陷预计参数反演中,研究了利用果蝇算法反演概率积分法开采沉陷预计参数的基本原理,构造了下沉拟合值与实测值均方差最小的适应度函数模型。结合安徽省某煤矿的实测数据,分别采用果蝇算法、遗传算法以及粒子群算法反演概率积分法开采沉陷预计参数,并以下沉拟合值与实测值的均方差为各算法反演精度的评价标准进行对比分析,结果表明：利用果蝇算法反演出的下沉拟合值与实测值的均方差（33.7 mm）以及相对中误差（1.4%）均小于同类条件下遗传算法、粒子群算法的反演结果,说明果蝇算法适用于反演概率积分法开采沉陷预计参数,对于提高概率积分法开采沉陷预计的精度有一定的参考价值。

英文摘要：

In order to solve the problems of complexity,large amount of calculating the mining subsidence prediction paramters inversion of the probability integral method,the fruit flies algorithm with the characteristics of simple,low computational complexity and high precision is introduced to the mining subsidence prediction parameters inversion of the probability integral method. The basic principle of the mining subsidence prediction parameters inversion of the probability integral method based on fruit flies algorithm is studied in depth. The fitness function model of minimum mean square of the subsidence fitting values and measured values is established. Based on the mining subsidence measured data of a coal mine in Anhui province,the mining subsidence prediction parameters inversion of the probability integral method are conducted by using the fruit flies algorithm,genetic algorithm and particle swarm algorithm respectively,the mean square error of the subsidence fitting values and measured values is taken as the evaluation criteria of the above three algorithms,the comparison results show that the mean square error of the subsidence fitting values and measured values is 33. 7 mm,the relative mean error is 1. 4%,which are lower than that of the genetic algorithm and particle swarm algorithm,therefore,it is further indicated that the fruit flies algorithm is suitable to conduct mining subsidence prediction parameters inversion of the probability integral method,it has some reference for improving the mining subsidence prediction precision of the probability integral method.