# Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical-Operational Management
## 3 Experiments Design and Result
### Execution and Experiment Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IG** & **Strategic Constraints Levels** & \(f(\overrightarrow{x})\) & \(e(f(\overrightarrow{x}))\) & \(\widetilde{f(x)}\) \\ \hline
1 & A & 0.986900 & 0.000467 & 0.986783 \\
1 & A-F & 0.987565 & 0.002054 & 0.986796 \\
1 & A-F-C & 0.987428 & 0.001752 & 0.986874 \\
1 & A-F-C-EO & 0.987183 & 0.000835 & 0.986812 \\
2 & A & 0.986767 & 0.000559 & 0.986670 \\
2 & A-F & 0.986803 & 0.000595 & 0.986680 \\
2 & A-F-C & 0.987025 & 0.001132 & 0.986647 \\
2 & A-F-C-EO & 0.987557 & 0.001130 & 0.987106 \\
3 & A & 0.986939 & 0.001223 & 0.986627 \\
3 & A-F-C & 0.986777 & 0.000664 & 0.986639 \\
3 & A-F-C & 0.986740 & 0.000527 & 0.986625 \\
3 & A-FC-EO & 0.988609 & 0.001446 & 0.988549 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fitness evaluation of the scalarization function. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**IG** &
\begin{tabular}{c} **Strategic** \\ **Constraints** \\ **Levels** \\ \end{tabular} & \(\mathbf{\rho}(\overrightarrow{x})\) & \(e(f1(\overrightarrow{x}))\) & \(\widetilde{f1(\overrightarrow{x})}\) & \(\mathbf{\rho}(\overrightarrow{x})\) & \(\widetilde{f2(\overrightarrow{x})}\) & \(\widetilde{f2(\overrightarrow{x})}\) & \(\mathbf{\beta}(\overrightarrow{x})\) & \(\sigma(\mathcal{B}(\overrightarrow{x}))\) & \(\widetilde{f3(\overrightarrow{x})}\) \\ \hline
1 & A & 1.00 & 0.00 & 1.00 & 0.804147 & 0.009005 & 0.801489 & 0.669295 & 0.017062 & 0.665523 \\
1 & A-F-C & 1.00 & 0.00 & 1.00 & 0.817280 & 0.041293 & 0.801489 & 0.667423 & 0.015376 & 0.662577 \\
1 & A-F-C & 1.00 & 0.00 & 1.00 & 0.814755 & 0.035440 & 0.801489 & 0.669001 & 0.016742 & 0.663130 \\
1 & A-F-C-EO & 1.00 & 0.00 & 1.00 & 0.811145 & 0.016723 & 0.801489 & 0.662561 & 0.011586 & 0.659791 \\
2 & A & 1.00 & 0.00 & 1.00 & 0.803195 & 0.011234 & 0.800561 & 0.666074 & 0.010372 & 0.658164 \\
2 & A-F & 1.00 & 0.00 & 1.00 & 0.803958 & 0.012015 & 0.800561 & 0.660461 & 0.00944 & 0.658408 \\
2 & A-F-C & 1.00 & 0.00 & 1.00 & 0.8068690 & 0.022899 & 0.800561 & 0.659105 & 0.008397 & 0.656217 \\
2 & A-F-C-EO & 1.00 & 0.00 & 1.00 & 0.820165 & 0.022698 & 0.809813 & 0.654826 & 0.005065 & 0.653115 \\
3 & A & 1.00 & 0.00 & 1.00 & 0.807198 & 0.024698 & 0.800419 & 0.657923 & 0.006619 & 0.656296 \\
3 & A-F-C & 1.00 & 0.00 & 1.00 & 0.803646 & 0.013384 & 0.800359 & 0.659479 & 0.009102 & 0.656734 \\
3 & A-F-C & 1.00 & 0.00 & 1.00 & 0.802865 & 0.010542 & 0.800359 & 0.65965 & 0.009007 & 0.657471 \\
3 & A-F-C-EO & 1.00 & 0.00 & 1.00 & 0.841527 & 0.028920 & 0.839940 & 0.653301 & 0.003522 & 0.652164 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fitness evaluation of the three objective functions. Regarding FLECO's ability to explore the solution space, Figure 3 presents 12 charts, each corresponding to an analysis case where 225 solutions are displayed (2700 in total). These charts depict the impact of applying constraints to the problem and its consequent effect on the constrained solution space. The results indicate that the feasible solution space becomes more fragmented as the number of decision variables is increased (which is the same as increasing the corresponding IG) and when more constraints are imposed on the problem, ranging from 1 to 22 in the designed experiments. Nonetheless, what is noteworthy is that the FLECO algorithm managed to explore these narrow segments of the constrained solutions space, in search of solutions, at an adequate level.