# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results
### Experimental Results for Communication Cost Analysis  
By examining Tables 5 and 6, we can determine the following values: for skill 2, the values were 0.1 and 0.05; for skill 3, the values were 0.1 and 0.04; for skill 4, the values were 0.1 and 0.04; for skill 5, the values were 0.1 and 0.04; for skill 6, the values were 0.1 and 0.03; for skill 7, the values were 0.1 and 0.03; for skill 8, the values were 0.11 and 0.04; for skill 9, the values were 0.34 and 0.03; and for skill 10, the values were 0.37 and 0.04. The communication cost parameter values for the remaining five heuristics ranged from 0.1 to 5.96 for the Academia Stack Exchange dataset and from 0.02 to 1.62 for the ACM dataset. For the Academia Stack Exchange dataset, the performance of I-PSO-Jaya becomes notably better as the number of skills increases. This is illustrated in experiments 6 through 9, where I-PSO-Jaya achieves the lowest communication costs, significantly outperforming other algorithms. For example, in experiment 9, with 10 skills and 30 iterations, I-PSO-Jaya achieved a communication cost of 0.37, compared to the next best cost of 0.48 by I-PSONSO, 5.07 by I-GWO, and 4.97 by I-JMSO. Similarly, for the ACM dataset, in experiment 5, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Exp. No.**} & \multirow{2}{*}{**No. of Skills**} & **No. of** & \multirow{2}{*}{**Exp. No.**} & \multirow{2}{*}{**No. of Skills**} & **No.** \\  & & & **Iterations** & & & **of Iterations** \\ \hline
1 & 2 & 5 & 6 & 7 & 30 \\ \hline
2 & 3 & 10 & 7 & 8 & 30 \\ \hline
3 & 4 & 15 & 8 & 9 & 30 \\ \hline
4 & 5 & 20 & 9 & 10 & 30 \\ \hline
5 & 6 & 25 & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters of the I-PSO-Jaya algorithm. \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} & **JYA** & **I-JMSO** & **GWO** & **I- GWO** & **I-PSONSO** &
\begin{tabular}{c} **I-PSO-** \\ **JYA** \\ \end{tabular} \\ \hline
1 & 2 & 5 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\ \hline
2 & 3 & 10 & 0.32 & 0.32 & 0.3 & 0.1 & 0.1 \\ \hline
3 & 4 & 15 & 0.74 & 0.7 & 0.66 & 0.66 & 0.1 & 0.1 \\ \hline
4 & 5 & 20 & 1.16 & 1.2 & 1.24 & 1.13 & 0.1 & 0.1 \\ \hline
5 & 6 & 25 & 1.64 & 1.7 & 1.87 & 1.55 & 0.1 & 0.1 \\ \hline
6 & 7 & 30 & 3.00 & 2.71 & 2.46 & 1.86 & 0.14 & 0.1 \\ \hline
7 & 8 & 30 & 3.61 & 2.96 & 2.64 & 2.79 & 0.25 & 0.11 \\ \hline
8 & 9 & 30 & 4.00 & 3.79 & 3.98 & 3.9 & 0.51 & 0.34 \\ \hline
9 & 10 & 30 & 5.23 & 4.97 & 5.96 & 5.07 & 0.48 & 0.37 \\ \hline \end{tabular}
\end{table} 
Table 5: Communication cost of I-PSO-Jaya compared to existing methods using the Academia Stack Exchange dataset. with 6 skills and 25 iterations, I-PSO-Jaya achieved a communication cost of 0.03, while the next best cost was 0.04 by I-PSONSO, 0.33 by I-GWO, and 0.38 by I-JMSO. In experiments where more skills are required, I-PSO-Jaya consistently achieves the lowest communication costs. The results indicate that I-PSO-Jaya excels, particularly when the required skills increase, as evidenced by the lower communication costs in complex scenarios. Efficiency of the I-PSO-Jaya algorithm for the Academia Stack Exchange dataset: By examining Table 5, the results from experiments 7, 8, and 9 demonstrate the better efficiency of the I-PSO-Jaya algorithm compared to the other methods tested. In these experiments, the communication cost for I-PSO-Jaya remains consistently lower than that of the other algorithms. Specifically, in experiment 7, the communication cost for I-PSO-Jaya is 0.11, significantly lower than the 0.25 to 3.61 range observed for the other methods. This trend continues in experiments 8 and 9, where I-PSO-Jaya achieves communication costs of 0.34 and 0.37, respectively, outperforming all other methods, which range from 0.51 to 4.00 for experiment 8 and 0.48 to 5.96 for experiment 9.