# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results
### Experimental Results for Standard Deviation of Communication Cost Analysis  
By examining Tables 9 and 10, we can determine the following values: for skill 2, the values were 0.0655 and 0.0546; for skill 3, the values were 0.1359 and 0.0855; for skill 4, the values were 0.1503 and 0.1483; for skill 5, the values were 0.2184 and 0.1234; for skill 6, the values were 0.2768 and 0.0796; for skill 7, the values were 0.3714 and 0.0697; for skill 8, the values were 0.4155 and 0.0577; for skill 9, the values were 0.5036 and 0.0401; and for skill 10, the values were 0.5014 and 0.0605. The communication cost parameter values for the five heuristic techniques ranged from 0.045 and 0.7213 for the Academia Stack Exchange dataset and from 0.0318 and 0.9441 for the ACM dataset. We specifically highlight the difference between I-PSO-Jaya and I-PSONSO for skills 6 to 10; the table shows the increased difference between them.  
\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** \\ **I-JMSO** \\ \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 & 0.0468 & 0.0534 & 0.0437 & 0.0450 & 0.0799 & 0.0655 \\ \hline
2 & 3 & 10 & 0.1124 & 0.1155 & 0.1081 & 0.1199 & 0.1487 & 0.1359 \\ \hline
3 & 4 & 15 & 0.1737 & 0.1854 & 0.1796 & 0.1744 & 0.1753 & 0.1503 \\ \hline
4 & 5 & 20 & 0.2295 & 0.2195 & 0.2244 & 0.2073 & 0.2210 & 0.2184 \\ \hline
5 & 6 & 25 & 0.2847 & 0.2888 & 0.3142 & 0.2863 & 0.2827 & 0.2768 \\ \hline
6 & 7 & 30 & 0.4115 & 0.4125 & 0.3893 & 0.4514 & 0.3723 & 0.3714 \\ \hline
7 & 8 & 30 & 0.4265 & 0.4311 & 0.4938 & 0.5242 & 0.5011 & 0.4155 \\ \hline
8 & 9 & 30 & 0.6287 & 0.6212 & 0.5609 & 0.5305 & 0.5098 & 0.5036 \\ \hline
9 & 10 & 30 & 0.7111 & 0.7213 & 0.6475 & 0.6065 & 0.5199 & 0.5014 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Standard deviation of the communication cost of I-PSO-Jaya and existing methods for the Academia Stack Exchange dataset.  
In the Academia Stack Exchange dataset, the I-PSO-Jaya algorithm shows a marked improvement in reducing the standard deviation of communication costs across all experiments. For instance, in experiment 9, with 10 skills and 30 iterations, the standard deviation for I-PSO-Jaya is 0.5014, significantly lower than the next best algorithm, I-PSONSO, which has a standard deviation of 0.5199, I-GWO at 0.6065, and 0.7213 at I-JMSO. For the ACM dataset, notable examples include: experiment 9, with 10 skills and 30 iterations, where I-PSO-Jaya records a standard deviation of 0.0605, whereas the next best algorithm is I-PSONSO at 0.0691, I-GWO at 0.8774, and I-JMSO at 0.9014. In experiment 5, with 6 skills and 25 iterations, I-PSO-Jaya achieves a standard deviation of 0.0796, compared to I-PSONSO's 0.0826.  
These results underscore the algorithm's effectiveness at maintaining low variability in communication costs, a crucial aspect that ensures more predictable and reliable team formation. This further validates the algorithm's ability to produce consistent outcomes, making it highly effective for complex team formation tasks where stability is crucial.