# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results
### Experimental Results for Sample Mean Communication Cost Analysis  
By examining Tables 7 and 8, we can determine the following values: for skill 2, the values were 0.0477 and 0.0502; for skill 3, the values were 0.1681 and 0.1004; for skill 4, the values were 0.238 and 0.1904; for skill 5, the values were 0.3595 and 0.1405; for skill 6, the values were 0.4214 and 0.060; for skill 7, the values were 0.7422 and 0.0704; for skill 8, the values were 0.9864 and 0.0842; for skill 9, the values were 1.3622 and 0.0450; and for skill 10, the values were 1.6313 and 0.0600. The communication cost parameter values for the remaining five heuristics ranged from 0.0613 to 7.2398 for the Academia Stack Exchange dataset and from 0.0511 to 3.5789 for the ACM dataset. We specifically \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Exp.** \\ **No.** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **Skill** \\ \end{tabular} & \begin{tabular}{c} **Iterat-** \\ **ions** \\ \end{tabular} & \begin{tabular}{c} **JAYA** \\ \end{tabular} & \begin{tabular}{c} **I-JMSO** \\ \end{tabular} & \begin{tabular}{c} **GWO** \\ \end{tabular} & \begin{tabular}{c} **I-GWO** \\ \end{tabular} & \
\begin{tabular}{c} **I-PSONSO** \\ \end{tabular} &
\begin{tabular}{c} **I-PSO-** \\ **JAYA** \\ \end{tabular} \\ \hline
1 & 2 & 5 & 0.02 & 0.02 & 0.02 & 0.02 & 0.05 & 0.05 \\ \hline
2 & 3 & 10 & 0.08 & 0.03 & 0.07 & 0.06 & 0.06 & 0.04 \\ \hline
3 & 4 & 15 & 0.17 & 0.13 & 0.22 & 0.18 & 0.04 & 0.04 \\ \hline
4 & 5 & 20 & 0.19 & 0.18 & 0.23 & 0.18 & 0.04 & 0.04 \\ \hline
5 & 6 & 25 & 0.43 & 0.38 & 0.41 & 0.33 & 0.04 & 0.03 \\ \hline
6 & 7 & 30 & 0.71 & 0.5 & 0.71 & 0.4 & 0.04 & 0.03 \\ \hline
7 & 8 & 30 & 0.86 & 0.76 & 0.86 & 0.86 & 0.05 & 0.04 \\ \hline
8 & 9 & 30 & 1.22 & 1.04 & 1.08 & 1.06 & 0.04 & 0.03 \\ \hline
9 & 10 & 30 & 1.62 & 1.14 & 1.49 & 1.14 & 0.05 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Communication cost of I-PSO-Jaya compared to existing methods using the ACM dataset. differentiate I-PSO-Jaya from I-PSONSO by focusing on skills 6 to 10. The table illustrates the increased discrepancy between the two approaches. The performance improvement becomes more pronounced as the number of required skills increases. For instance, in experiment 9, with 10 skills and 30 iterations, I-PSO-Jaya achieved a communication cost of 1.6313, significantly lower than the next best cost of 1.7036 by I-PSONSO, 7.0825 by I-GWO, and 7.1301 by I-JMSO. Similarly, for the ACM dataset, in experiment 9, with 10 skills and 30 iterations, I-PSO-Jaya attained a communication cost of 0.0600, compared to the following best cost of 0.0619 by I-PSONSO, 3.5659 by I-GWO, and 3.5789 by I-JMSO. When compared to existing algorithms, I-PSO-Jaya outperforms I-JMSO by 77.48%, I-GWO by 77.49%, and I-PSONSO by up to 6.11% on the Academia Stack Exchange dataset. Additionally, for the ACM dataset, I-PSO-Jaya surpasses I-JMSO by 93.86%, I-GWO by 93.83%, and I-PSONSO by up to 6.41%. This indicates that I-PSO-Jaya is particularly effective in complex scenarios requiring extensive skill coverage, as demonstrated by its exceptional performance in all experiments and superiority to all earlier algorithms. Efficiency of the I-PSO-Jaya algorithm on the Academia Stack Exchange dataset: By examining Table 7, the analysis of the sample mean communication costs from the Academia Stack Exchange dataset further emphasizes the efficiency of the I-PSO-Jaya algorithm, particularly in the more complex experiments 7, 8, and 9. In experiment 7, the I-PSO-Jaya