# An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm
## 5 Conclusions  
There have been a number of improved grey wolf optimizer (GWO) algorithms in recent years, including an improved GWO algorithm for initial population selection and threshold selection. This paper proposed an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm. The GWO algorithm combines multithreshold OTSU, Tsallis entropy, and DE algorithms. Multithreshold OTSU calculates the fitness in the initial population and makes the initial \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **The Threshold Value** & **RI** & **GCE** & **VI** \\ \hline DE\(-\)OTSU\(-\)GWO & 123/161/228 & 0.9493 & 0.0518 & 0.3095 \\ GWO & 71/140/199 & 0.8930 & 0.0633 & 0.3950 \\ DE\(-\)GWO & 49/145/189 & 0.9196 & 0.0609 & 0.3627 \\ PSO & 71/148/206 & 0.9328 & 0.0596 & 0.3448 \\
2D\(-\)OTSU\(-\)FA & 97/145/192 & 0.8885 & 0.0680 & 0.4043 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The assessment results of segmentation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Implication** & **Symbol** & **Implication** \\ \hline \(F(x)\) & The threshold of algorithm x & \(i\) & label \\ n & Scale of problem & \(\sum\limits_{i}n_{ij}\) & the number of points with \(i\) in the \(S\) \\ T(n) & Time frequency & \(\sum\limits_{j}n_{ij}c\) & the number of points with \(j\) in the \(S^{\prime}\) \\ \(S_{1}\) and \(S_{2}\) & Segmentation & \(N\) & the total number of points \\ \(p_{q}\) & pixel & \(C\) & clustering \\ \(R\Big{(}S,p_{q}\Big{)}\) & a region in segmentation \(S\) with \(p_{q}\) & \(H(C)\) & Entropy of \(C\) \\ \(n\) & the number of pixels & \(I(C_{1},C_{2})\) & the mutual information between \(C_{1}\) and \(C_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The implication of the formula symbol in Section 4.  
stage basically stable, and not affected by image brightness and contrast. Tsallis entropy quickly calculates the fitness. The DE algorithm can solve the local optimal solution of the GWO algorithm. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing PSO and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving benchmark functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. In the sensitivity detection of each algorithm, for the first and the third thresholds, the OTSO-GWO is sensitive compared with GWO and DE-GWO, and the difference is obvious. In the second threshold, the DE-GWO is sensitive compared with OTSU-GWO, but the difference is insignificant. In terms of time complexity, after adding the DE algorithm, the time complexity increases by O(18n4). However, the OTSU algorithm needs to traverse each grey value and classify it, so the overall time is increased. Adding the DE algorithm can solve the local optimal solution of GWO and the time will be reduced. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in the detection of RI, GCE, and VI. The segmentation results of the DE-OTSU-GWO algorithm are close to the results of manual detection. Conceptualization, Y.L., J.S., and Y.W. ; Data curation, Y.L., J.S., and X.Z. ; Formal analysis, Y.L. and H.Y. ; Investigation, Y.L. and H.Y. ; Methodology, Y.L. and J.S. ; Supervision, H.Y. and Y.W. ; Validation, Y.L., J.S., and Y.W. ; Writing--original draft, J.S. and X.Z. ; Writing--review & editing, Y.L., H.Y., and Y.W. All authors have read and agreed to the published version of the manuscript.