## 4 Experimental Setup  
To achieve the effective working of the algorithms, it is compulsory to adjust the parameters coupled with all approaches to their most suitable value. Largely, these parameters are observed before the implementation of the algorithm and maintain uniformity throughout the execution. In various studies, it is suggested that the most appropriate methodology for selecting the parameters of any algorithm is predicted through exhaustive experiments for obtaining the optimal parameters. In this study, the experimental setting of the parameters is employed in Table 1 and the parameters setting of the algorithms in Table 2, respectively, which is on the basis of the literature stated in this section. Along with this, objective functions and their details are in Table 3. The search space boundary is \([-100,100]\) population size kept 50 with 10 number of runs. Further, 10, 20, and 30 dimensions are used for 1000, 2000, and 3000 iterations, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & & \\ \hline Search Space & [100, \(-100\)] & & \\ Dimensions & 10 & 20 & 30 \\ Iterations & 1000 & 2000 & 3000 \\ Population size & 50 & & \\ Number of PSO Runs & 10 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setting of parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr\#** & **Function Name** & **Objective Function** & **Search Space** & **Optimal Value** \\ \hline
01 & Sphere & \(Minf(x)=\sum\limits_{i=1}^{D}x_{i}^{2}\) & \(-5.12\leq x_{i}\leq 5.12\) & 0 \\
02 & Rastrigin & \(Minf(x)=10D+\sum\limits_{i=1}^{D}\left[x_{i}^{2}-10\cos(2\pi x)\right]_{i}\) & \(-5.12\leq x_{i}\leq 5.12\) & 0 \\
03 & Axis parallel hyper-ellipsoid & \(Minf(x)=\sum\limits_{i=1}^{D}(i.x_{i}^{2})\) & \(-5.12\leq x_{i}\leq 5.12\) & 0 \\
04 & Rotated hyper ellipsoid & \(Minf(x)=\sum\limits_{i=1}^{D}\sum\limits_{i=1}^{i}\left(x_{i}^{2}\right)\) & \(-65.536\leq x_{i}\leq 65.536\) & 0 \\
05 & Moved Axis & \(Minf(x)=\sum\limits_{i=1}^{D}5i.x_{i}^{2}\) & \(-5.12\leq x_{i}\leq 5.12\) & 0 \\
06 & Sum of different power & \(Minf(x)=\sum\limits_{i=1}^{D}\left[x_{i}\right]^{(i+1)}\) & \(-1\leq x_{i}\leq 1\) & 0 \\
07 & [PERSON] & \(Minf(x)=\left(\sum\limits_{i=1}^{D}x_{i}^{2}\right)^{2}\) & \(-100\leq x_{i}\leq 100\) & 0 \\
08 & [PERSON] & \(Minf(x)=\sum\limits_{i=1}^{D}x_{i}^{2}\left(2+\sin\frac{1}{x_{i}}\right)\) & \(-1\leq x_{i}\leq 1\) & 0 \\
09 & [PERSON] & \(Minf(x)=0.5+\frac{\sin^{2}\left(x_{i}^{2}+x_{i}^{2}\right)^{2}-0.5}{10+0.001(x_{ i}^{2}+x_{i}^{2})^{2}}\) & \(-100\leq x_{i}\leq 100\) & 0 \\
10 & [PERSON]\_Steiglitz & \(Minf(x)=\sum\limits_{i=1}^{D}x_{i}^{4}\) & \(-100\leq xi\leq 100\) & 0 \\
11 & Schwefel & \(Minf(x)=\left(\sum\limits_{i=1}^{D}x_{i}^{2}\right)^{a}\) & \(-100\leq x_{i}\leq 100\) & 0 \\
12 & Schwefell1.2 & \(Minf(x)=\sum\limits_{i=1}^{D}\left(\sum\limits_{i=1}^{D}x_{i}\right)^{2}\) & \(-100\leq x_{i}\leq 100\) & 0 \\
13 & Schwefel 2.21 & \(Minf(x)=\max\limits_{i=1}^{D}\left[x_{i}\right]\) & \(-100\leq x_{i}\leq 100\) & 0 \\
14 & Schwefel 2.22 & \(Minf(x)=\sum\limits_{i=1}^{D}\left[x_{i}\right]+\frac{D}{i=1}\left[x_{i}\right]\) & \(-100\leq x_{i}\leq 100\) & 0 \\
15 & Schwefel 2.23 & \(Minf(x)=\sum\limits_{i=1}^{D}x_{i}^{10}\) & \(-10\leq x_{i}\leq 10\) & 0 \\
16 & [PERSON] & \(Minf(x)=\sum\limits_{i=1}^{D}x_{i}^{2}+\left(\sum\limits_{i=1}^{D}\sum\limits_{ i=1}^{n}i_{i}\right)^{2}+\left(\sum\limits_{i=1}^{D}\sum\limits_{i=1}^{n}i_{i} \right)^{4}\) & \(-5\leq x_{i}\leq 10\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters setting of parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameters** \\ \hline \(PSO\) & c1=c2=1.49, w = linearly decreasing \\ \(BA\) & \(N_{p}\)=40, \(r_{ij}^{t}\in[0,1]\), \(A_{ij}^{t}\in[0,2]\) \\ \(DE\) & F \(\in[0.4,1]\), CR \(\in\) 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Function table with characteristics.