# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results
### Experimental Results for Sample Mean Communication Cost Analysis
\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} & **JAYA** & **I-JMSO** & **GWO** & **I-GWO** & **I-PSONSO** &
\begin{tabular}{c} **I-PSO-** \\ **JAYA** \\ \end{tabular} \\ \hline
1 & 2 & 5 & 0.1493 & 0.1583 & 0.1521 & 0.1506 & 0.0613 & 0.0477 \\ \hline
2 & 3 & 10 & 0.4945 & 0.5006 & 0.4891 & 0.5106 & 0.2037 & 0.1681 \\ \hline
3 & 4 & 15 & 1.0252 & 1.0152 & 1.0267 & 0.9961 & 0.2613 & 0.2380 \\ \hline
4 & 5 & 20 & 1.6476 & 1.6576 & 1.6394 & 1.6393 & 0.3822 & 0.3595 \\ \hline
5 & 6 & 25 & 2.4332 & 2.4442 & 2.4501 & 2.4339 & 0.4638 & 0.4214 \\ \hline
6 & 7 & 30 & 3.4512 & 3.4602 & 3.4684 & 3.4471 & 0.7762 & 0.7422 \\ \hline
7 & 8 & 30 & 4.3431 & 4.3511 & 4.5462 & 4.5491 & 1.0685 & 0.9864 \\ \hline
8 & 9 & 30 & 5.7231 & 5.7332 & 5.7983 & 5.6608 & 1.4243 & 1.3622 \\ \hline
9 & 10 & 30 & 7.1246 & 7.1301 & 7.2398 & 7.0825 & 1.7036 & 1.6313 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sample mean communication cost of I-PSO-Jaya compared to existing methods using the Academia Stack Exchange dataset. \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}  & **JAYA** & **I-JMSO** & **GWO** & **I-GWO** & **I-PSONSO** & \begin{tabular}{c} **I-PSO-** \\ **JAYA** \\ \end{tabular}  \\ \hline
1 & 2 & 5 & 0.0608 & 0.0656 & 0.345 & 0.0583 & 0.0511 & 0.0502 \\ \hline
2 & 3 & 10 & 0.1732 & 0.1755 & 0.735 & 0.1841 & 0.1094 & 0.1004 \\ \hline
3 & 4 & 15 & 0.3657 & 0.3913 & 0.786 & 0.3816 & 0.1960  
algorithm achieves a mean communication cost of 0.9864, which is significantly lower than the costs associated with other methods, which range from 1.0685 to 4.5491. This efficiency is consistently observed in experiment 8, where I-PSO-Jaya's mean communication cost is 1.3622, outperforming all other methods that exhibit mean costs between 1.4243 and 5.7983. Similarly, in experiment 9, I-PSO-Jaya maintains a lower mean communication cost of 1.6313, while the other methods range from 1.7036 to 7.2398. Efficiency of the I-PSO-Jaya algorithm for the ACM dataset: By examining Table 8, the sample mean communication costs from the ACM dataset further underline the efficiency of the I-PSO-Jaya algorithm, particularly in the more complex experiments 7, 8, and 9. In experiment 7, I-PSO-Jaya achieves a mean communication cost of 0.0842, which is markedly lower than the other methods, whose costs range from 0.10059 to 2.4557. This trend continues in experiment 8, where I-PSO-Jaya records a mean communication cost of just 0.0450, significantly outperforming all other methods, which range from 0.0530 to 2.9607. Similarly, in experiment 9, I-PSO-Jaya maintains a low mean communication cost of 0.0600, while other methods show higher costs ranging from 0.0619 to 3.5789. These results consistently demonstrate that I-PSO-Jaya achieves significantly lower sample mean communication costs compared to other algorithms, especially as the complexity of the problems increases. This efficiency highlights the robustness and effectiveness of the I-PSO-Jaya algorithm in solving complex optimization problems while minimizing communication costs. These results demonstrate that I-PSO-Jaya consistently achieves lower sample mean communication costs compared to other algorithms, particularly as the complexity of the problem increases. This highlights the superior efficiency of the I-PSO-Jaya algorithm in handling complex optimization scenarios.