基于改进麻雀优化算法的概率积分法参数反演

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中图分类号：TD327 文献标志码：A 文章编号：1008-0562（2025）04-0395-08

Parameter inversion of probability integral method based on improved sparrow optimization algorithm BAIJicheng'，WANGJianmin²*，LIXiao'，LIYanhui'，ZHANGZhijun1 （1.No.1 Mine，Pingdingshan Tian'an Coal Industry Company Limited， Pingdingshan 467000， China;2. School of Geomatics，Liaoning Technical University， Fuxin 123ooo， China）

Abstract： In order to solve the problem that the sparrow optimization algorithm （SSA）has slow convergence speed and is easy to fall into local optimal solution in the parameter inversion calculation of mining subsidence prediction model，an improved sparrow optimization algorithm （ISSA） is proposed.The algorithmadds Kent mapping to the population initialization process to enhance the uniform distribution of population individuals.The foragingbehavior of the parrotoptimization algorithm is introduced in the location update of the discoverer，and the safety value is adjusted according to the fitnessInorder to improve the performance of the algorithm，thet disturbance distribution and the lens reverse learning strategy are combined.WOA，SSA and ISSA are used to invert the parameters of probability integral method （PIM）respectively.The PIM with inversion parameters is used to simulate the sinking value，anti-rough error interference ability and anti-random error of the experimental working face.The results showthat compared with WOA and SSA，the PIM simulation effectusing ISSA inversion parameters is the best.The ISSA isapplied toan engineering example，and the results show that thecalculated value of the PIM model using ISSA inversion parameters is closer to the actual value.The research conclusion provides a reference for improving the accuracy of subsidence prediction and mining area disaster detection.

Keywords： probability integral method; sparrow search algorithm; parrot optimization algorithm; Kent mapping; lens-based reverse;learningminingsubsidence

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