# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results
### Confidence Intervals  
Confidence intervals are typically used to determine the range of values a population parameter, such as a mean or a difference between means, is likely to be found within. When comparing two or more algorithms, we evaluate the uncertainty surrounding performance measures and utilize confidence intervals to judge if our suggested approach is statistically significantly superior to the alternative algorithm. Computing CI can be achieved by following these steps:  
\[CI=mean\pm confidence. \tag{6}\]  
The value of \(\gamma\) is determined by the confidence level (i.e., \(\gamma=1-\text{C}\)) and the standard deviation of the sample. The sample size is equal to the size of the population. When utilizing a 95% confidence interval (CI), the population mean (\(\gamma=1-0.95=0.05\)) can be determined by referring to Tables 7-10.  
Here, we may locate confidence intervals to assess how well our algorithms perform in comparison to earlier algorithms, which can determine the performance improvement in percentage terms:  
\[P(\%)=\left[\frac{\left(\text{Avg}(\text{Jaya-JMSO,GWO,I-GWO,I-PSONSO}) \right)-\left(\text{Avg}(\text{I-PSO-Jaya})\right)}{\text{Avg}(\text{Jaya,I-MSO,GWO,I-GWO,I-PSONSO})} \right]\times 100, \tag{7}\]  
where Avg(Jaya,I-JMSO,GWO, I-GWO, I-PSONSO), and \(P\) denotes the performance. The average results achieved from the standard Java, I-JMSO, GWO, I-GWO, I-PSONSO, and I-PSO-Jaya algorithms are represented by Avg(I-PSO-Jaya).  
**Confidence intervals for the Academia Stack Exchange dataset.** The CI of average (mean) communication is displayed in Table 7. The cost is provided for 09 experiments on the Stack Exchange platform. Collection of data: The data presented in Table 7 illustrate the mean communication cost for experiments 1 to 9, with the total of 2 skills increasing by one up to 10. For the range of iterations from 5, increase by five up to 30, inclusive. The experiment outcomes are displayed in Table 7. I-PSO-Jaya communication cost decreases progressively with each iteration compared to other algorithms: the maximum performance improvement obtained is 73.51%.  
**Confidence intervals for the ACM dataset.** The CI of average communication is displayed in Table 8. The cost is provided for 9 experiments on the ACM platform. The data presented in Table 8 illustrates the mean communication cost for experiments 1 to 9, with 2 skills increasing by 1 up to 10. The experiment outcomes are displayed in Table 8. The I-PSO-Jaya communication cost decreases progressively with each iteration compared to other algorithms: the maximum performance improvement obtained is 92.69%.  
Our proposed approach is designed to provide an optimized solution when more skills are required rather than fewer skills, which gives better results in the base alternative  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Exp. No.**} & \multirow{2}{*}{**Iterations**} & \multicolumn{2}{c}{**Std Communi-**} & \multicolumn{2}{c}{**Mean Communication Cost**} & \multicolumn{1}{c}{**Computing Time**} & \multicolumn{1}{c}{**Communication Cost**} \\ \hline
1 & 5 & 0.5153 & 1.8065 & 10.5293 & 0.54 \\ \hline
2 & 10 & 0.0754 & 0.6384 & 17.4571 & 0.13 \\ \hline
3 & 15 & 0.2635 & 0.4016 & 32.5742 & 0.05 \\ \hline
4 & 20 & 0.2926 & 0.3435 & 33.7902 & 0.05 \\ \hline
5 & 25 & 0.2970 & 0.3152 & 49.7784 & 0.05 \\ \hline
6 & 30 & 0.2992 & 0.3107 & 50.2374 & 0.04 \\ \hline
7 & 35 & 0.3109 & 0.3035 & 55.2734 & 0.04 \\ \hline
8 & 40 & 0.3316 & 0.2778 & 83.0970 & 0.04 \\ \hline
9 & 45 & 0.3854 & 0.1643 & 87.5645 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Convergence analysis of the I-PSO-Jaya algorithm for the ACM dataset.  
approach. This arises because our algorithm is specifically tuned to handle complex, multi-skilled team formations and may not be as efficient in simpler contexts where a more straightforward solution could suffice.