# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 4 Performance Testing and Comparison  
CEC2022 benchmark functions are a standard test set designed by the IEEE Computational Intelligence Society for evaluating the performance of optimization algorithms. It contains different types of optimizations problems, including single-peaked functions (for testing local search capability), multi-peaked functions (for testing global search capability), hybrid functions, and combinatorial functions (for testing the performance of algorithms in complex scenarios). These functions have different characteristics, such as multimodality, non-separability, rotation, etc., which enable a thorough evaluation of the optimization techniques' performance. The IGOOSE method was simulated using CEC2022 benchmark functions to examine its performance. Single-peak (F1), multimodal (F2-F5), hybrid (F6-F8), and mixed (F9-F12) functions are among the functions used. These functions were picked because they can thoroughly test the algorithm's performance and represent many kinds of optimization challenges. Through these studies, we can confirm the IGOOSE algorithm's capacity for optimization in a range of situations and guarantee its dependability for use in the future when predicting subclinical mastitis in dairy cows. This article uses 10 dimensions. The specific function information is shown in Table 1.  
This study compared IGOOSE with the DBA, HBA, GWO, HHO, and GOOSE algorithms to confirm its superiority and efficacy. The comparison algorithms were set to a population size of N = 30, a maximum number of iterations of T = 1000, and each algorithm was executed 30 times independently to guarantee the fairness of the comparison findings. In Table 2, the statistical findings are displayed. (1) Table 2 shows that, for 11 of the test functions, the algorithm results are nearly at the hypothetical ideal values, except for the test results for F6. According to the experimental data, IGOOSE achieves the theoretical optimal value for the unimodal function F1 and exhibits notable optimization performance. The theoretical best value is reached by F4 for the multimodal functions F2 through F5. The theoretical best value is reached by F6 and F8 for the mixed functions F6 to F8. Every one of the combined functions F9-F12 reaches the supposedly ideal value. In F1, F3, F4, F8, F9, F10, F11, and F12, the extremely low standard deviation shows that IGOOSE is more stable than the other algorithms. In this study, we also selected six representative functions from Table 2--F1, F3, F6, F7, F10 and \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **No.** & **Function** & **F\({}_{\text{min}}\)** \\ \hline Unimodal Function & 1 & Shifted and full Rotated Zakharoy Function & 300 \\ \hline \multirow{4}{*}{Basic Functions} & 2 & Shifted and full Rotated Rosenbrock’s Function & 400 \\  & 3 & Shifted and full Rotated Expanded Schaffer’s F6 Function & 600 \\  & 4 & Shifted and full Rotated Non-Continuous Rastrigin’s & 800 \\  & 5 & Shifted and full Rotated Levy Function & 900 \\ \hline \multirow{4}{*}{Hybrid Functions} & 6 & Hybrid Function 1 (N = 3) & 1800 \\  & 7 & Hybrid Function 2 (N = 6) & 2000 \\  & 8 & Hybrid Function 3 (N = 5) & 2200 \\ \hline \multirow{4}{*}{Composition Functions} & 9 & Composition Function 1 (N = 5) & 2300 \\  & 10 & Composition Function 1 (N = 5) & 2400 \\ \cline{1-1}  & 11 & Composition Function 1 (N = 5) & 2600 \\ \cline{1-1}  & 12 & Composition Function 1 (N = 5) & 2700 \\ \hline \hline \end{tabular}  
* Search range: [\(-\)100, 100]
* means 10 dimensions.  
\end{table} 
Table 1: CEC2022 benchmark functions. F12--and plotted convergence graphs for 30 runs. As seen in Figure 3, the ordinate denotes the function's average fitness value, while the abscissa indicates the number of iterations. (2) From the process point of view, in the single-peak function F1, despite starting the iteration with a somewhat slower rate of convergence than the HHO and GWO algorithms, the IGOOSE algorithm achieves the theoretical optimum after roughly 400 iterations.