## 5 Experimental Analysis of the Cybersecurity Optimizer
### Results and Discussion for Cybersecurity Optimizer in Solving CEC2022 Optimization Problems
This performance surpasses that of competing algorithms such as Chimp, CMAES, and GWO, which display higher variability in their results. The low mean and SEM values underscore the optimizer's efficiency in quickly and accurately reaching optimal solutions. In F2, the CYO continues to perform strongly, with a mean of \(4.18\times 10^{2}\), securing a top position (2 nd place). This result indicates the optimizer's adaptability to medium-difficulty functions. In comparison to algorithms like CPO and ROA, which rank lower and exhibit higher standard deviations, the CYO demonstrates superior stability and consistency. Across more challenging functions such as F5, F7, and F10, the CYO consistently ranks within the top three, demonstrating its resilience and adaptability. In F5, the optimizer achieves a mean value of \(9.12\times 10^{2}\) with a standard deviation of \(2.29\times 10^{1}\), highlighting its competitive performance despite the increased complexity of the problem. The optimizer's consistent high ranking across functions of varying difficulty underscores its robust balance between exploration and exploitation, both of which are critical for optimizing complex landscapes. The performance of the CYO in F6 is particularly remarkable, as it attains a mean value of \(1.81\times 10^{3}\), outperforming several other algorithms that exhibit higher variability and larger mean values. Its low standard error and minimal variability emphasize the reliability of the CYO, especially when applied to functions that demand precise exploration of the solution space. The CYO consistently outperforms traditional algorithms such as CMAES, WOA, and Chimp across the CEC2022 benchmark suite. Not only does it achieve superior mean values, but it also exhibits lower standard deviations, indicating greater stability and efficiency. This exceptional performance is a testament to the optimizer's dynamic adaptability to diverse problem landscapes, ensuring a strong balance between diversification (exploration) and intensification (exploitation). Similarly, when applied to the CEC2017 benchmark functions (F1-F15), as shown in Table 5, the CYO continues to demonstrate superior performance across most test cases. It consistently ranks within the top three for functions such as F1, F2, and F5, underscoring its robust optimization capabilities across a wide range of function types. For instance, in F2, the optimizer achieves the lowest mean of \(4.53\times 10^{2}\) with a small standard deviation, significantly outperforming algorithms such as Chimp, CPO, and ROA. This result highlights the optimizer's ability to converge rapidly on the optimal solution while maintaining a low error margin, a critical factor in achieving high accuracy for complex optimization tasks. In F5 and F6, which represent more challenging optimization landscapes, the optimizer maintains low standard deviations and ranks among the top performers. Its ability to navigate these complex landscapes while striking a balance between exploration and exploitation is evident from its superior mean values and lower SEM, ensuring a more stable and reliable search process compared to other methods. Even in more difficult functions, such as F8, F10, and F12, the CYO remains competitive, frequently ranking within the top three. Its performance across these functions demonstrates its versatility and adaptability, positioning it as a robust option for addressing a wide range of optimization problems. The results from the CEC2017 benchmark functions (F16-F30) further validate the optimizer's capabilities, as shown in Table 6. In functions such as F16, F17, and F18, the CYO consistently ranks first or second, demonstrating its proficiency in solving complex optimization problems. The optimizer's low mean values and standard errors in these functions highlight not only its superior convergence speed but also its reliability, as it consistently delivers performance with minimal variability.