## 5 Experimental Analysis of the Cybersecurity Optimizer
### Results and Discussion for Cybersecurity Optimizer in Solving CEC2022 Optimization Problems
For instance, in F17, the optimizer achieves the best mean value of \(2.04\times 10^{3}\), along with a low standard deviation, significantly outperforming other algorithms such as CPO and ROA, which exhibit greater variability and lower rankings. The optimizer's capacity to maintain stable performance, even in complex functions like F18 and F19, underscores its robustness and its effective balance between exploration and exploitation strategies. In functions such as F20, F21, and F22, the CYO continues to perform strongly, securing top ranks and demonstrating its adaptability across diverse optimization challenges. The consistently low standard error of the mean (SEM) across these functions highlights the optimizer's ability to maintain reliable performance, positioning it as a robust candidate for complex, real-world applications. Key Insights from the Results:  
Consistency Across Functions: The CYO consistently ranks highly across diverse functions, demonstrating both versatility and robustness. Low Mean and Standard Error: The optimizer achieves some of the lowest mean values and standard errors, indicating faster convergence and more accurate solutions compared to competing algorithms. Low Variability: The low standard deviation in several functions highlights the stability of the optimizer, which is crucial for high-stakes applications like cybersecurity. Effective Balance of Exploration and Exploitation: The results clearly demonstrate the optimizer's capacity to balance the exploration of new regions with the exploitation of known high-quality solutions, a critical factor in efficiently navigating complex, high-dimensional problem spaces. Superior Performance in Challenging Functions: The optimizer consistently ranks highly in difficult functions such as F5, F7, F10, and F18, showcasing its adaptability to a broad range of problem complexities and difficulties. These results validate the efficacy of the CYO as a highly effective and reliable optimization technique, especially in dynamic, multi-dimensional problem landscapes, such as those encountered in cybersecurity applications. The CYO demonstrates superior performance across various CEC2017 benchmark functions (F1-F15) compared to other algorithms, as shown in Table 5. It consistently ranks highly, often achieving the best or near-best mean values and standard errors. For instance, in functions such as F1 through F6, the CYO ranks first or second, showcasing its robust optimization capabilities. It outperforms several algorithms, including Chimp, CPO, and ROA, particularly in F2, where it achieves the lowest mean and standard error. Additionally, the optimizer demonstrates stability, as reflected by the low standard deviations across several functions, such as F5 and F6. Even in more challenging functions like F8, F10, and F12, the optimizer remains competitive, frequently ranking within the top three. The CYO continues to demonstrate its effectiveness across the CEC2017 benchmark functions (F16-F30), as shown in Table 6, maintaining strong performance against a diverse array of algorithms. It secures top ranks in several functions, such as F16, F17, F18, and F19, where it consistently achieves first or second place, indicating its ability to efficiently navigate complex optimization landscapes. The optimizer's superior mean values and lower standard errors in these functions underscore its robustness and reliability. For instance, in F16, F17, and F18, it not only achieves the lowest mean values but also exhibits lower variability, reflecting stable performance. Compared to other algorithms like Chimp, CPO, and ROA, which often display higher standard deviations and lower rankings, the CYO demonstrates a balanced approach to exploration and exploitation. This is particularly evident in challenging functions such as F18 and F19, where the optimizer significantly outperforms others by maintaining lower mean and standard error values. Additionally, its performance in functions like F20, F21, and F22 further highlights its versatility and adaptability.