## 4 Experimental Setup
### Parameter Configuration and Settings  
Eight binary variants of metaheuristic algorithms were employed for a comparative analysis with the method proposed in this study, i.e., the binary dwarf mongoose optimizer (BDMO) [14], the binary simulated normal distribution optimizer (BSNDO), the binary particle swarm optimizer (BPSO), the binary whale optimization algorithm (BWOA), the binary sailfish optimizer (BSFO), the binary grey wolf optimizer (BGWO), BEOSA and BIEOSA. Table 2 lists the parameter settings applied for each of the algorithms. The values for the parameters \(\pi\), \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\) and \(\beta_{4}\), as used in our experiments with BEOSA and BIEOSA, are listed in the table as 0.1, 0.1, 0.1, 0.1 and 0.1, respectively. Similarly, the values for nb, na, ns, peep, \(\tau\) and L were set at 3, (N-nb), (n-nb), 1, [0,1] and round (0.6*D*nb) for BDMO. The mean of the population size was computed to set the mo parameter of the BSNDO. Additionally, the BPSO control parameters c1, c2, W and Vmax were initialized at 2, 2, 0.9 and 6. The parameters for the remaining algorithms are shown in the table, with p, l, b r, and C for BWOA being initialized at [0,1], [0,1], 1, [0,1] and 2r. In BSFO, p was 0.1. A was 4 and epsilon was 0.001. Lastly, BGWO was initialized within the bound [2, 0] as coefficient for decreasing power attack.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Parameter** & **Value** & **Definition** \\ \hline BEOSA & \(\pi\) & 0.1 & Recruitment rate \\  & \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\) and \(\beta_{4}\) & 0.1, 0.1, 0.1, and 0.1 & Contact rate of infected individuals, of the host, with the dead \\ BIEOSA & \(\pi\) & 0.1 & Recruitment rate \\  & \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\) and \(\beta_{4}\) & 0.1, 0.1, 0.1, and 0.1 & Contact rate of infected individuals, of the host, with the dead \\ BDMO & nb & 3 & Number of babystitles \\  & na & N-nb & Number of alpha \\  & ns & N-nb & Number of subordinates \\  & peep, \(\tau\) & 1, rand (0, 1) & Peep sound, tau operator for fitness evaluation \\  & L & round (0.6*D*nb) & Babystitler exchange parameter \\ BSNDO & mo & mean(N) & Mean position of the population \\ BPSO & c1, c2 & 2, 2 & Positive learning factors constant 1 and constant 2 \\  & W & 0.9 & Initial weight \\  & Vmax & 6 & Maximum velocity vector \\ BWOA & p, l & [0, 1], [-1, 1] & Random number, random number \\  & b & 1 & Shape of spiral \\  & r, C & [0, 1], 2r & Random vector, coefficient vector \\ BSFO & Pp & 0.1 & Percentage of the sending population \\  & A, \(\varepsilon\) & 4, 0.001 & The coefficient for decreasing power attack \\ BGWO & au & [2,0] \\ \hline \end{tabular}
\end{table}
Table 2: Parameters for the BEOSA, BIEOSA, BDMO, BSNDO, BPSO, BWOA, BSFO and BGWO metaheuristic algorithms in this study. N, as used for BDMO and BSNDO, denotes the population size.  
Population sizes of 25, 50, 75 and 100 were investigated for each of the algorithms to show how this variable affected performance. The training of the algorithms followed 50 iterative processes, and the experiment for each algorithm was typically repeated 10 times/runs to determine the average performance. The formulae applied to compute these averages and all other similar metrics used for our comparative analysis are presented in the following subsection.