Technical gazette, Vol. 28 No. 2, 2021.
Original scientific paper
https://doi.org/10.17559/TV-20200429150210
Parameter Solving of Probability Integral Method Based on Improved Genetic Algorithm
Jingxian Li
; 1) School of Earth and Environment, Anhui University of Science and Technology; 2) School of Geomatics, Anhui University of Science and Technology; 3) Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes; 4) Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology
Xuexiang Yu*
; School of Geomatics, Anhui University of Science and Technology, No. 168, Taifeng Street, Huainan City, Anhui Province, 232001, China
Ya Liang
; School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, 232001, China
Shenshen Chi
; School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, China
Abstract
The probability integral method (PIM) is the main method for mining subsidence prediction in China. Parameter errors and model errors are the main sources of error in the application of the probability integral method. There are many surface subsidence problems caused by coal mining. In order to improve the accuracy and operating efficiency of the genetic algorithm (GA) in calculating the parameters of the PIM, this paper proposes an improved genetic algorithm (IGA) by adding the dynamic crossover and mutation rate to the traditional GA. Made improvements to the shortcomings of random crossover and mutation rate of all individuals in the population in the original algorithm.Through simulation experiments, it is confirmed that the IGA improves the calculation efficiency and accuracy of the traditional GA under the same conditions.The IGA has higher accuracy, reliability, resistance to gross interference and resistance to missing observation points. This method is obviously superior to direct inversion and conventional optimization inversion algorithms, and effectively avoids the dependence on the initial value of the probabilistic integral method parameter.
Keywords
dynamic crossover; genetic algorithm; ground subsidence; mutation rate
Hrčak ID:
255821
URI
Publication date:
17.4.2021.
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