# IDEINFO: An Improved Vector-Weighted Optimization Algorithm
## 4 Discussion
### Wilcoxon Rank Sum Test
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**F** & **Index** & **IDEINFO** & **INFO** & **SSA** & **GWO** & **WOA** & **INFO1** & **INFO2** \\ \hline \multirow{4}{*}{F7} & Best & \(-11205.917\) & \(-11365.255\) & \(-\mathbf{12569.485}\) & \(-7610.723\) & \(-12569.482\) & \(-10928.05\) & \(-12094.216\) \\ \cline{2-7}  & Mean & \(-10218.713\) & \(-8662.457\) & \(-9140.772\) & \(-\mathbf{5864.470}\) & \(\mathbf{-11201.082}\) & \(-9034.592\) & \(-10451.007\) \\ \cline{2-7}  & Std & 592.216 & \(731.942\) & \(2642.386\) & \(717.885\) & \(1541.727\) & \(641.344\) & \(617.033\) \\ \hline \multirow{4}{*}{F8} & Best & \(\mathbf{4.4409\times 10^{-16}}\) & \(\mathbf{4.4409\times 10^{indicating that there is a significant difference between the two algorithms; for \(p>5\%\), the H0 hypothesis is accepted, indicating that the two algorithms have the same overall performance. Table 7 compares the IDEINFO, INFO1, INFO2,INFO, SSA, WOA, and GWO algorithms for 14 benchmark tests. The Wilcoxon rank sum test on 14 benchmark functions is presented in Table 7. It indicates the comparable performance between the two, where \"Na\" is \"not applicable\"; i.e., no significant test can be performed. For the results of the significance tests, \"+\", \"--\", and \"=\", indicate that the performance of IDEINFO is better or worse than that of the compared algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**F** & **Index** & **IDEINFO** & **INFO** & **SSA** & **GWO** & **WOA** & **INFO1** & **INFO2** \\ \hline \multirow{4}{*}{F11} & Best & 0.00030749 & 0.00030749 & 0.00030749 & 0.00030749 & **0.00030752** & 0.0030749 & 0.00030749 \\ \cline{2-9}  & Mean & 0.0011592 & 0.00033496 & 0.00031349 & 0.0010126 & 0.00058225 & **0.0043524** & 0.0041142 \\ \cline{2-9}  & Std & 0.0003646 & 0.0001566 & \(\mathbf{6.6315\times 10^{-5}}\) & 0.0037102 & 0.00040975 & 0.0014248 & 0.00032207 \\ \hline \multirow{4}{*}{F12} & Best & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) \\ \cline{2-9}  & Mean & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) & \(\mathbf{-1.0316}\) \\ \cline{2-9}  & Std & \(6.7752\times 10^{-16}\) & \(6.7752\times 10^{-16}\) & \(3.905\times 10^{-9}\) & \(4.2802\times 10^{-9}\) & \(1.3367\times 10^{-10}\) & \(\mathbf{6.7122\times 10^{-16}}\) & \(\mathbf{6.7122\times 10^{-16}}\) \\ \hline \multirow{4}{*}{F13} & Best & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4027}\) & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4029}\) \\ \cline{2-9}  & Mean & \(\mathbf{-10.4029}\) & \(\mathbf{-8.6849}\) & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4028}\) & \(\mathbf{-8.1503}\) & \(\mathbf{-8.6849}\) & \(\mathbf{-9.7031}\) \\ \cline{2-9}  & Std & \(\mathbf{1.8067\times 10^{-15}}\) & \(3.1747\) & \(1.2781\times 10^{-5}\) & \(9.0224\times 10^{-5}\) & \(3.1151\) & \(3.1747\) & \(2.1403\) \\ \hline \multirow{4}{*}{F14} & Best & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) \\ \cline{2-9}  & Mean & \(\mathbf{-10.5364}\) & \(\mathbf{-9.8192}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5363}\) & \(\mathbf{-9.4995}\) & \(\mathbf{-10.0462}\) & \(\mathbf{-10.313}\) \\ \cline{2-9}  & Std & \(\mathbf{1.6493\times 10^{-15}}\) & \(2.1989\) & \(1.8401\times 10^{-5}\) & \(8.0884\times 10^{-5}\) & \(2.3942\) & \(1.8886\) & \(1.2234\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results for four low-latitude benchmark test functions.