# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 6 Parameter-Setting of the Optimization Technique
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & **BESSA** & \multicolumn{2}{c}{**BES**} & \multicolumn{2}{c}{**CamWOA**} & \multicolumn{2}{c}{**WOA**} \\ \cline{2-9}  & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** \\ \hline \(f_{8}(p)\) & \(-34949.9\) & \(330.9\) & \(-3222.5\) & \(391.2\) & \(-7339.503\) & \(947.3066\) & \(-122.69\) & \(543.2189\) \\ \(f_{8}(p)\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \(f_{8}(p)\) & \(8.882\times 10^{-16}\) & \(0\) & \(8.88\times 10^{-16}\) & \(0\) & \(8.88\times 10^{-16}\) & \(0\) & \(3.84\times 10^{-15}\) & \(2.1035\times 10^{-15}\) \\ \(f_{8}(p)\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0.042819\) & \(0.12435\) \\ \(f_{8}(p)\) & \(4.712\times 10^{-3}\) & \(0\) & \(4.701\times 10^{-32}\) & \(0\) & \(0.002626\) & \(0.003269\) & \(1.18\times 10^{-6}\) & \(7.6666\times 10^{-7}\) \\ \(f_{8}(p)\) & \(0.9934\) & \(0.42\theta\) & \(0.2628\) & \(0.4697\) & \(0.0086723\) & \(0.006867\) & \(7.15\times 10^{-6}\) & \(6.449\times 10^{-4}\) \\ \hline \multicolumn{9}{c}{**WCMFO**} & \multicolumn{2}{c}{**ABC**} & \multicolumn{2}{c}{**PSOGSA**} \\ \cline{2-9}  & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** \\ \hline \(f_{8}(p)\) & \(-3729.7\) & \(96.325\) & \(-3922.73\) & \(86.61857\) & \(-3271.6\) & \(278.08\) & \(-3712.01\) & \(167.4447\) \\ \(f_{8}(p)\) & \(2089\) & \(1.508\) & \(3.677\) & \(1.0365\) & \(23.821\) & \(12.968\) & \(6.574\) & \(1.367\) \\ \(f_{8}(p)\) & \(8.88\times 10^{-16}\) & \(1.0\times 10^{-31}\) & \(1.21\times 10^{-6}\) & \(9.37\times 10^{-7}\) & \(4.94\times 10^{-12}\) & \(2.26\times 10^{-12}\) & \(1.24\times 10^{-15}\) & \(1.08\times 10^{-15}\) \\ \(f_{8}(p)\) & \(9.91\times 10^{-2}\) & \(5.31\times 10^{-2}\) & \(0.281\) & \(0.1086\) & \(0.2004\) & \(0.1141\) & \(3.96\times 10^{-2}\) & \(8.8\times 10^{-3}\) \\ \(f_{8}(p)\) & \(2.0\times 10^{-29}\) & \(6.44\times 10^{-29}\) & \(1.9\times 10^{-3}\) & \(1.3\times 10^{-3}\) & \(0.2941\) & \(0.581\) & \(9.77\times 10^{-3}\) & \(1.3\times 10^{-4}\) \\ \(f_{8}(p)\) & \(4.49\times 10^{-22}\) & \(2.06\times 10^{-21}\) & \(8.3\times 10^{-3}\) & \(5.1\times 10^{-3}\) & \(3.11\times 10^{-21}\) & \(1.066\times 10^{-21}\) & \(1.31\times 10^{-9}\) & \(1.39\times 10^{-9}\) \\ \hline \multicolumn{9}{c}{**MFO**} & \multicolumn{2}{c}{**GSA**} & \multicolumn{2}{c}{**WCA**} & \multicolumn{2}{c}{**DA**} \\ \cline{2-9}  & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** & **Avg.** & **Std. Dev** \\ \hline \(f_{8}(p)\) & \(-3329.13\) & \(288.317\) & \(-1664.53\) & \(190.6721\) & \(-3422.55\) & \(304.572\) & \(-3213.66\) & \(431.748\) \\ \(f_{8}(p)\) & \(12.8372\) & \(7.325\) & \(1.392\) & \(1.214\) & \(20.993\) & \(1.524\) & \(11.561\) & \(10.177\) \\ \(f_{8}(p)\) & \(8.8\times 10^{-16}\) & \(1.0\times 10^{-31}\) & \(1.28\times 10^{-10}\) & \(6.71\times 10^{-11}\) & \(2.42\times 10^{-15}\) & \(1.79\times 10^{-15}\) & \(3.14\times 10^{-5}\) & \(1.7\times 10^{-4}\) \\ \(f_{8}(p)\) & \(1.78\times 10^{-1}\) & \(8.43\times 10^{-2}\) & \(1.67\times 10^{-2}\) & \(2.79\times 10^{-2}\) & \(0.1502\) & \(9.44\times 10^{-2}\) & \(0.3846\) & \(0.3826\) \\ \(f_{8}(p)\) & \(3.11\times 10^{-2}\) & \(9.487\times 10^{-2}\) & \(7.95\times 10^{-2}\) & \(3.23\times 10^{-2}\) & \(1.036\times 10^{-2}\) & \(5.67\times 10^{-2}\) & \(0.5296\) & \(0.6912\) \\ \(f_{8}(p)\) & \(1.10\times 10^{-3}\) & \(3.33\times 10^{-3}\) & \(5.67\times 10^{-20}\) & \(1.88\times 10^{-30}\) & \(7.3\times 10^{-4}\) & \(2.7\times 10^{-3}\) & \(0.5292\) & \(0.7173\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multimodal benchmark functions statistical outcomes.