# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 6 Parameter-Setting of the Optimization Technique
Dev** \\ \hline \(f_{1}(p)\) & \(1.10\times 10^{-16}\) & \(6.05\times 10^{-18}\) & \(1.93\times 10^{-2}\) & \(1.46\times 10^{-2}\) & \(1.24\times 10^{-20}\) & \(3\times 10^{-21}\) & \(9.48\times 10^{-13}\) & \(1.46\times 10^{-2}\) \\ \(f_{3}(p)\) & \(3.52\times 10^{-33}\) & \(1.35\times 10^{-32}\) & \(3.42\times 10^{-2}\) & \(1.3\times 10^{-2}\) & \(2.58\times 10^{-10}\) & \(4.57\times 10^{-1}\) & \(1.67\times 10^{-5}\) & \(1.3\times 10^{-2}\) \\ \(f_{5}(p)\) & \(1.62\times 10^{-32}\) & \(8.83\times 10^{-32}\) & **84.34** & 227.45 & \(2.48\times 10^{-20}\) & \(9.34\times 10^{-21}\) & \(2.17\times 10^{-6}\) & \(227.45\) \\ \(f_{6}(p)\) & \(3.29\times 10^{-24}\) & \(1.31\times 10^{-23}\) & 7.939 & 1.938 & \(6.35\times 10^{-11}\) & \(1.26\times 10^{-11}\) & \(4.9\times 10^{-13}\) & 1.9808 \\ \(f_{7}(p)\) & \(2.4259\) & \(3.467\) & \(44.513\) & 15.196 & 1.1607 & \(2.0941\) & \(0.68946\) & \(0.57142\) \\ \(f_{6}(p)\) & \(6.97\times 10^{-29}\) & \(3.29\times 10^{-28}\) & \(1.13\times 10^{-2}\) & \(7.3\times 10^{-3}\) & \(1.38\times 10^{-2}\) & \(3.37\times 10^{-21}\) & \(1.48\times 10^{-2}\) & \(8.3\times 10^{-13}\) \\ \(f_{7}(p)\) & \(0.4987\) & \(0.305\) & \(3.93\times 10^{-2}\) & \(1.09\times 10^{-2}\) & \(2.3\times 10^{-3}\) & \(1.2\times 10^{-3}\) & \(4.735\times 10^{-3}\) & \(1.754\times 10^{-3}\) \\ \hline \multicolumn{2}{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 \(f_{1}(p)\) & \(1.65\times 10^{-31}\) & \(4.91\times 10^{-31}\) & \(1.02\times 10^{-18}\) & \(3.3\times 10^{-19}\) & \(3.1\times 10^{-14}\) & \(5.8\times 10^{-14}\) & \(5.03\times 10^{-1}\) & 1.3180 \\ \(f_{2}(p)\) & \(2.69\times 10^{-19}\) & \(6.22\times 10^{-19}\) & \(2.33\times 10^{-19}\) & \(4.39\times 10^{-10}\) & \(2.11\times 10^{-7}\) & \(3.9\times 10^{-2}\) & 2.392 & 3.912 \\ \(f_{3}(p)\) & \(2. _Appraisal and Analysis of Exploration Capability (p8(y)-p13(y))_  
Multimodal functions are characterized by having a number of local optima, thus testing on these functions appraise the exploration capability of any metaheuristic algorithm. Table 3 lists the multimodal functions. Table 4 enumerates the statistical results of the different algorithms gathered for 30 runs on every benchmark function. The statistical outcomes of the Hybrid BESSA, BES, CamWOA, and the other ten optimization techniques [9] tested on the multimodal functions (\(f_{8}(p)-f_{13}(p)\)), are reported in Table 4, and it was found that the Hybrid BESSA technique produced better results for all of the mentioned functions except function \(f_{13}(p)\). For function \(f_{13}(p)\), the BES produced a better result than the Hybrid BESSA orsCamWOA. For function \(f_{10}(p)\), the results are comparable. _Appraisal and Analysis of Exploration Capability (Testing on Fixed Dimension Multimodal Functions, \(f_{14(p)}-f_{23(p)}\))_  
The fixed dimensional multimodal functions (\(f_{8}(p)-f_{13}(p)\)) and their outcomes are displayed in Tables 5 and 6. It was found that, when tested upon the fixed dimensional