## 3 Improved Grey Wolf Optimizer Algorithm to Optimize Support Vector Machine
### Improved GWO Algorithm
It applied the ICEEMDAN method to process the cutting sound signal of the shearer and extracted the characteristic IMF component through the correlation coefficient. Moreover, the extension of the CMPE model, based on permutation entropy, made it possible to effectively extract the characteristic information of the cutting sound signal, which helped to improve the accuracy of the shearer cutting pattern recognition. The paper established the IGWO-SVM cutting pattern recognition model by introducing the DE algorithm and the nonlinear convergence factor. Furthermore, it improved the global search capability of GWO, avoided the problem of the algorithm falling into the local optimum in the later stage, and promoted the convergence speed at the same time. On this foundation, the cutting experiment was carried out through the simulation cutting experiment system. The average recognition rate of the cutting pattern reached 97.67%, which was better than the commonly used classification and recognition models. Therefore, this method provided a new research idea for precisely recognizing shearer cutting patterns based on cutting sound signals. However, the method in this paper also had some shortcomings. On the one hand, it did not consider the influence of noise on the experimental results. The underground environments of coal mines are harsh, and the intense noise will affect the accuracy of the cutting sound signal acquisition and analysis. On the other hand, the experiment only analyzed the discrete data and did not recognize the continuous cutting sound signal. Therefore, the authors plan to conduct theoretical analyses and experimental research on sound signal denoising and on-line cutting pattern recognition systems in future research. Conceptualization, T.P. and C.L. ; methodology, T.P. and C.L. ; software, C.L. ; validation, T.P., C.L. and Y.Z. ; formal analysis, C.L. ; resources, T.P. ; data curation, C.L. ; writing--original draft preparation, C.L. ; writing--review and editing, T.P. ; project administration, T.P. ; funding acquisition, T.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 51475001). Not applicable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Recognition Model** & \begin{tabular}{c} **Average Accuracy** \\ **Rate/\%** \\ \end{tabular} & \begin{tabular}{c} **Average Recognition** \\ **Time/s** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy Error/\%** \\ \end{tabular} &
\begin{tabular}{c} **Time-Consuming** \\ **Error/s** \\ \end{tabular} \\ \hline ABC-SVM & 96.11 & 0.4 & 3.34 & 0.21 \\ PSO-SVM & 95.61 & 0.74 & 3.89 & 0.28 \\ GWO-SVM & 96.11 & 0.28 & 0 & 0.19 \\ IGWO-SVM & 97.67 & 0.27 & 0.56 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of four cutting pattern recognition models. Data Availability Statement: The data present in this study are available on request from the corresponding author. ## Conflicts of Interest: The authors declare no conflict of interest. ## References

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