# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Func** & **Criteria** & **SMIGWO** & **MGWO** & **SPTLBO** & **ChoA** & **WChOA** & **RL-ChoA** \\ \hline \multirow{4}{*}{F1} & Best & 9.29 \(\times\) 10\({}^{-30}\) & 3.45 \(\times\) 10\({}^{-47}\) & 4.30 \(\times\) 10\({}^{-104}\) & 4.82 \(\times\) 10\({}^{-10}\) & 6.70 \(\times\) 10\({}^{-288}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Mean & 9.41 \(\times\) 10\({}^{-28}\) & 8.27 \(\times\) 10\({}^{-45}\) & 7.17 \(\times\) 10\({}^{-101}\) & 2.78 \(\times\) 10\({}^{-06}\) & 7.14 \(\times\) 10\({}^{-281}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Worst & 6.18 \(\times\) 10\({}^{-27}\) & 8.33 \(\times\) 10\({}^{-44}\) & 7.74 \(\times\) 10\({}^{-100}\) & 1.94 \(\times\) 10\({}^{-05}\) & 1.16 \(\times\) 10\({}^{-279}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Std & 1.43 \(\times\) 10\({}^{-27}\) & 1.91 \(\times\) 10\({}^{-44}\) & 1.88 \(\times\) 10\({}^{-100}\) & 4.35 \(\times\) 10\({}^{-06}\) & 0.00 \(\times\) 10\({}^{+00}\) & 0.00 \(\times\) 10\({}^{+00}\) \\ \hline \multirow{4}{*}{F2} & Best & 2.02 \(\times\) 10\({}^{-17}\) & 4.11 \(\times\) 10\({}^{-28}\) & 7.53 \(\times\) 10\({}^{-55}\) & 6.41 \(\times\) 10\({}^{-09}\) & 2.19 \(\times\) 10\({}^{-146}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Mean & 6.61 \(\times\) 10\({}^{-17}\) & 7.16 \(\times\) 10\({}^{-27}\) & 2.61 \(\times\) 10\({}^{-53}\) & 2.83 \(\times\) 10\({}^{-05}\) & 1.22 \(\times\) 10\({}^{-144}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Worst & 1.47 \(\times\) 10\({}^{-16}\) & 3.75 \(\times\) 10\({}^{-26}\) & 2.37 \(\times\) 10\({}^{-52}\) & 1.48 \(\times\) 10\({}^{-04}\) & 9.24 \(\times\) 10\({}^{-144}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Std & 3.84 \(\times\) 10\({}^{-17}\) & 7.71 \(\times\) 10\({}^{-27}\) & 4.81 \(\times\) 10\({}^{-53}\) & 3.67 \(\times\) 10\({}^{-05}\) & 1.77 \(\times\) 10\({}^{-144}\) & 0.00 \(\times\) 10\({}^{+00}\) \\ \hline \multirow{4}{*}{F3} & Best & 2.63 \(\times\) 10\({}^{-08}\) & 4.51 \(\times\) 10\({}^{-16}\) & 1.04 \(\times\) 10\({}^{-72}\) & 2.40 \(\times\) 10\({}^{+00}\) & 4.12 \(\times\) 10\({}^{-273}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Mean & 6.53 \(\times\) 10\({}^{-06}\) & 1.23 \(\times\) 10\({}^{-08}\) & 1.24 \(\times\) 10\({}^{-67}\) & 8.59 \(\times\) 10\({}^{+01}\) & 1.94 \(\times\) 10\({}^{-208}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Worst & 8.17 \(\times\) 10\({}^{-05}\) & 3.66 \(\times\) 10\({}^{-07}\) & 2.63 \(\times\) 10\({}^{-66}\) & 5.66 \(\times\) 10\({}^{+02}\) & 5.83 \(\times\) 10\({}^{-207}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Std & 1.57 \(\times\) 10\({}^{-05}\) & 6.67 \(\times\) 10\({}^{-08}\) & 4.86 \(\times\) 10\({}^{-67}\) & 1.21 \(\times\) 10\({}^{+02}\) & 0.00 \(\times\) 10\({}^{+00}\) & 0.00 \(\times\) 10\({}^{+00}\) \\ \hline \multirow{4}{*}{F4} & Best & 4.31 \(\times\) 10\({}^{-08}\) & 1.62 \(\times\) 10\({}^{-14}\) & 2.64 \(\times\) 10\({}^{-50}\) & 9.46 \(\times\) 10\({}^{-03}\) & 3.29 \(\times\) 10\({}^{-141}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Mean & 5.99 \(\times\) 10\({}^{-07}\) & 6.12 \(\times\) 10\({}^{-13}\) & 2.30 \(\times\) 10\({}^{-48}\) & 4.15 \(\times\) 10\({}^{-01}\) & 1.32 \(\times\) 10\({}^{-137}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Worst & 1.92 \(\times\) 10\({}^{-06}\) & 7.62 \(\times\) 10\({}^{-12}\) & 1.00 \(\times\) 10\({}^{-47}\) & 2.88 \(\times\) 10\({}^{+00}\) & 1.02 \(\times\) 10\({}^{-136}\) & 0.00 \(\times\) 10\({}^{+00}\) \\  & Std & 4.63 \(\times\) 10\({}^{-07}\) & 1.38 \(\times\) 10\({}^{-12}\) & 2.62