## 5 Experimental Outcomes
### Binary Classification Experiment  
Binary classification outcomes based on the objective function evaluations over 30 independent runs are shown in terms of best, worst, as well as mean and median in Table 1. Alongside these outcomes, the standard deviation and variance are provided in order to asses algorithm stability. Additional evaluations using the indicator function outcomes over 30 runs are provided in Table 2 with relative stability indicators. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline CNN-XG-GIRSA & **0.497019** & **0.497351** & **0.497172** & **0.497109** & 0.000130 & 1.69 \(\times\) 10\({}^{-8}\) \\ CNN-XG-RSA & 0.497301 & 0.497633 & 0.497464 & 0.497472 & **0.000106** & **1.13 \(\times\) 10\({}^{-8}\)** \\ CNN-XG-GA & 0.497190 & 0.497694 & 0.497480 & 0.497563 & 0.000183 & 3.37 \(\times\) 10\({}^{-8}\) \\ CNN-XG-PSO & 0.497109 & 0.497573 & 0.497369 & 0.497482 & 0.000184 & 3.38 \(\times\) 10\({}^{-8}\) \\ CNN-XG-GA & 0.497140 & 0.497663 & 0.497524 & 0.497623 & 0.000195 & 3.78 \(\times\) 10\({}^{-8}\) \\ CNN-XG-SSA & 0.497180 & 0.497532 & 0.497412 & 0.497502 & 0.000142 & 2.02 \(\times\) 10\({}^{-8}\) \\ CNN-XG-WOA & 0.497170 & 0.497754 & 0.497414 & 0.497402 & 0.000221 & 4.89 \(\times\) 10\({}^{-8}\) \\ CNN-XG-COLSHADE & 0.497049 & 0.497563 & 0.497305 & 0.497291 & 0.000195 & 3.82 \(\times\) 10\({}^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Objective function overall outcomes. An observation can already be made from the presented results. Models struggle with binary classification problems. Despite the poor performance shown by all models, the introduced algorithm attained the best outcome in comparison to other optimization algorithms even when limited performance is observed. Algorithm stability can be observed in Figure 2.  
An obvious advantage of the introduced algorithm is the overall better performance in comparison to other algorithms. However, a high rate of stability of the original RSA algorithm and FA needs to be noted despite inferior results in both indicator and objective evaluations. Convergence rates for each metaheuristic are tracked and plots can be observed in Figure 3.  
Improvements to the convergence rate can be noted, with the introduced algorithm no longer dwelling in less promising areas, and displaying a better exploration in compassion to the original, as well as competing metaheuristics. A comparison of all constructed models in detail is presented in Table 3. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline CNN-XG-GIBSA & **0.006218** & **0.005539** & **0.005908** & **0.006036** & 0.000265 & 7.02 \(\times\)\(10^{-8}\) \\ CNN-XG-RSA & 0.005650 & 0.004983 & 0.005342 & 0.005262 & **0.000244** & **5.95 \(\times\)\(10^{-8}\)** \\ CNN-XG-GA & 0.005905 & 0.004858 & 0.005321 & 0.005241 & 0.000368 & 1.35 \(\times\)\(10^{-7}\) \\ CNN-XG-PSO & 0.006036 & 0.005157 & 0.005576 & 0.005535 & 0.000348 & 1.21 \(\times\)\(10^{-7}\) \\ CNN-XG-FA & 0.005973 & 0.004978 & 0.005245 & 0.005062 & 0.000368 & 1.36 \(\times\)\(10^{-7}\) \\ CNN-XG-SSA & 0.005892 & 0.005151 & 0.005400 & 0.005199 & 0.000287 & 8.22 \(\times\)\(10^{-8}\) \\ CNN-XG-WOA & 0.005913 & 0.004775 & 0.005479 & 0.005714 & 0.000464 & 2.15 \(\times\)\(10^{-7}\) \\ CNN-XG-COLSHADE & 0.006158 & 0.005177 & 0.005632 & 0.005640 & 0.000394 & 1.55 \(\times\)\(10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Indicator function overall outcomes. Figure 3: Objective and indicator function convergence plots. Figure 2: Binary classification evaluation outcome distribution. As can be observed from the attained outcomes, implemented models struggle with the challenging task of handling intrusion detection through binary classification within an MQTT IoT system.