## 3 Methodology
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multicolumn{3}{c}{_Best-\(f_{best}\)_} & \multicolumn{3}{c}{_Mean-\(f_{best}\)_} & \multicolumn{3}{c}{_Std-\(f_{best}\)_} \\ \cline{2-10}  & **FIPS** & **SaDE** & **FIPSaDE** & **FIPS** & **SaDE** & **FIPSaDE** & **FIPS** & **SaDE** & **FIPSaDE** \\ \hline F1 & \(-297.49\) & \(-450.00\) & \(-450.00\) & \(550.32\) & \(-450.00\) & \(-450.00\) & 474.17 & **0.00** & **0.00** \\ F2 & \(2925.32\) & \(-334.80\) & \(-450.00\) & \(-7039.46\) & 141.37 & \(-450.00\) & 2090.36 & 394.74 & **0.00** \\ F3 & \(4662.131.370\) & \(8070.374\) & \(1929.179\) & \(9858.504\) & 300.49 & 720.45 & 886.641.45 & 2,9374.489.5 & 1,504,013.37 & 63,037.56 \\ F4 & \(3522.98\) & \(3339.36\) & \(-450.00\) & \(1269.098\) & 8382.96 & \(-417.79\) & 3289.58 & 3329.53 & **68.06** \\ F5 & \(2752.59\) & \(2851.22\) & \(1828.14\) & 4763.71 & 4550.14 & **2882.76** & 1143.85 & 1175.35 & **534.97** \\ F6 & \(1311.61025\) & \(393.12\) & \(390.00\) & \(4001.270\) & 534.04 & **391.20** & 41151.46 & 195.57 & **1.86** \\ F7 & \(**4516.29\) & \(**4516.29\) & \(**4516.29\) & **4516.29** & **4516.29** & **4516.29** & **4516.29** & **4516.29** & **0.00** & **0.00** \\ F8 & \(-119.18\) & \(-119.08\) & \(-119.24\) & \(-119.10\) & \(-118.95\) & \(-119.11\) & **0.05** & 0.06 & **0.05** \\ F9 & \(-274.12\) & \(-325.88\) & \(-329.01\) & \(-238.80\) & \(-311.72\) & \(-319.73\) & 15.94 & 7.87 & **4.57** \\ F10 & \(-257.68\) & \(-303.46\) & \(-307.12\) & \(-216.13\) & \(-212.55\) & \(-288.20\) & 19.61 & 52.41 & **9.80** \\ F11 & \(108.32\) & \(110.59\) & \(104.48\) & \(112.14\) & \(125.38\) & **1102.33** & **1.49** & 5.28 & 2.56 \\ F12 & \(44816.64\) & \(1924.29\) & \(-457.54\) & \(929.543.1\) & 1563.41 & **5948.00** & 3528.60 & 13,703.43 & **9314.81** \\ F13 & \(-125.85\) & \(-120.51\) & \(-128.55\) & \(-123.16\) & \(-117.97\) & \(-127.62\) & 1.59 & 1.15 & **0.59** \\ F14 & \(-288.87\) & \(-286.68\) & \(-289.13\) & \(-288.18\) & \(-286.46\) & \(-288.01\) & 0.33 & **0.13** & 0.50 \\ F15 & \(518.41\) & \(230.34\) & \(320.00\) & \(606.35\) & **447.49** & 455.94 & **45.94** & 106.58 & 97.05 \\ F16 & \(227.57\) & \(179.27\) & \(165.78\) & \(369.64\) & 309.45 & **249.52** & 151.59 & 138.42 & **125.34** \\ F17 & \(238.75\) & \(297.00\) & \(172.70\) & \(366.55\) & 418.69 & **268.73** & 141.11 & **92.36** & 109.60 \\ F18 & \(916.04\) & \(810.00\) & \(915.42\) & \(920.85\) & **918.09** & 922.17 & 6.09 & 21.18 & **5.81** \\ F19 & \(851.22\) & \(810.00\) & \(915.96\) & 917.97 & **917.60** & 921.58 & 19.54 & 20.83 & **4.63** \\ F20 & \(915.69\) & \(915.39\) & \(810.00\) & \(921.96\) & 923.04 & **918.81** & 7.84 & **4.70** & 20.93 \\ F21 & \(953.46\) & \(860.00\) & \(860.00\) & \(1292.43\) & **860.00** & 892.10 & 260.10 & **0.00** & 131.12 \\ F22 & \(1213.63\) & \(1245.75\) & \(1209.77\) & \(1256.86\) & 1305.60 & **1251.15** & **22.58** & 41.09 & 23.76 \\ F23 & \(901.56\) & \(896.74\) & **894.16** & **950.64** & 976.97 & 959.16 & **97.57** & 133.89 & 158.86 \\ F24 & \(550.61\) & \(460.00\) & \(460.00\) & \(675.47\) & **460.00** & 485.29 & 76.41 & 0.00 & 138.53 \\ F25 & \(1880.42\) & \(1891.12\) & \(1866.00\) & \(1891.27\) & 1904.77 & **1877.14** & 5.85 & 6.17 & **4.40** \\ \hline \hline \(h_{min}\) & \(1\) & \(7\) & \(\mathbf{22}\) & \(3\) & \(7\) & \(\mathbf{18}\) & \(6\) & \(6\) & \(\mathbf{16}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Best-\(f_{best}\), mean-\(f_{best}\) and std-\(f_{best}\) obtained by FIPS, SaDE and FIPSaDE algorithms for the settings of 30D30N._The overall findings from the comparisons show that FIPSaDE has the highest probability of producing a better solution compared to its original variants, which are FIPS and SaDE, with an increase in problem dimensionality and population size. An EC algorithm commonly uses a large population size to solve an optimization problem with high dimensionality. However, the approach may be more effective for FIPSaDE than FIPS and SaDE.