基于改进NGO算法的煤体应力反演

大直径钻孔卸压是防治煤矿冲击地压的有效手段之一，研究钻进过程煤体应力的变化情况对防止冲击地压有重要意义。目前关于钻进参数与煤体应力的关系模型研究较少且精度有限，对此本文提出了一种基于北方苍鹰优化算法(northern goshawk optimization,NGO)与支持向量回归(support vector regression,SVR)的PSO-NGO-SVR煤体应力反演模型。首先,该模型在NGO种群初始化阶段引入Tent混沌映射，并将粒子群算法（particle swarm optimization,PSO）的优势融入到北方苍鹰算法中，使改进后的北方苍鹰算法拥有更好的性能；接着,使用改进后的北方苍鹰算法对支持向量回归中的超参数迭代寻优;最后,以迭代后的最优超参数建立模型。结果表明:改进后北方苍鹰算法的敛速度和收敛精度有较大提升，PSO-NGO-SVR煤体应力反演模型拥有较高精度。

Research on coal stress inversion based on improved NGO

The large diameter borehole pressure relief is one of the effective means used to prevent rock burst in coal mines. It is of great significance to study the change of coal body stress during drilling to prevent the rock burst. At present, the relational model between drilling parameters and coal stress is rarely studied, and the precision is limited. Therefore, a new PSO-NGO-SVR coal stress inversion model was proposed based on northern goshawk optimization (NGO) and support vector regression (SVR) in this article. Firstly, the Tent chaotic mapping was introduced into the model during the initialization phase of the NGO population, and the advantages of particle swarm optimization (PSO) were integrated into the Northern Hawk algorithm, resulting in better performance of the improved Northern Hawk algorithm; Next, the improved Northern Eagle algorithm was used to iteratively optimize hyperparameters in support vector regression; Finally, the model was established with the optimal hyperparameters after iteration. The results show that the convergence speed and accuracy of the improved Northern Eagle algorithm are greatly improved, and the PSO-NGO-SVR coal stress inversion model has relatively high accuracy.