## 5 Experimental Analysis of the Cybersecurity Optimizer
### Results and Discussion for Cybersecurity Optimizer in Solving CEC2022 Optimization Problems  
Tables 3 and 4 provide a comprehensive comparison of the performance of the CYO against several state-of-the-art optimization algorithms. The comparison is based on the results obtained from testing various algorithms on the CEC2022 benchmark suite (functions F1 through F12) across different dimensions (Dim = 10 and Dim = 20) and with a fixed number of function evaluations (FES = 1000 and FES = 2000). Each table presents statistical metrics, including the mean, standard deviation (Std), and standard error of the mean (SEM) for each function, enabling a detailed evaluation of the CYO in terms of convergence speed, accuracy, and robustness. The results are compared with other optimization algorithms, such as the Chimp Optimizer, CMAES, GWO, and HHO, among others. For each function, the algorithms are ranked according to their performance, as shown in the tables. These rankings offer a clear indication of the relative standing of the CYO compared to its peers across various problem landscapes, highlighting both its strengths and any areas where its performance may be less competitive. This detailed ranking facilitates an in-depth analysis of the CYO in both low- and high-dimensional optimization problems. Furthermore, the comparative performance of the CYO across various CEC2022 benchmark functions, as summarized in Tables 3 and 4, demonstrates its competitive and often superior optimization capabilities. For function F1, the CYO achieved a significantly lower mean value compared to other well-established algorithms, such as Chimp, CPO, and ROA, underscoring its effective exploitation capabilities in simpler landscapes. In more complex scenarios, such as function F5, the optimizer continues to deliver robust performance, closely matching or even outperforming advanced optimizers like HHO and MFO. This trend is consistent across several test functions, where the CYO frequently ranks among the top performers. Notably, its adaptability is further emphasized in mixed-modality \begin{table}
\begin{tabular}{l l l} \hline \hline
**Acronym** & **Algorithm Name** & **Year** \\ \hline SSOA & Synergistic Swarm Optimization Algorithm [55] & 2024 \\ FLO & Filled Lizard Optimization [56] & 2024 \\ CPO & Chinese Pangolin Optimizer [57] & 2024 \\ FVM & Four Vector Optimizer [58] & 2024 \\ SHIO & Success History Intelligent Optimizer [59] & 2022 \\ ZOA & Zebra Optimization Algorithm [60] & 2022 \\ DOA & Dingo Optimization Algorithm [61] & 2021 \\ ROA & Remora Optimization Algorithm [62] & 2021 \\ AO & Aquila Optimizer [63] & 2021 \\ CHMP & Chimp Optimization Algorithm [64] & 2020 \\ STOA & Souty Term Optimization Algorithm [65] & 2019 \\ SOA & Seagell Optimization Algorithm [66] & 2019 \\ SCSO & Sand Cat Optimization Algorithm [67] & 2023 \\ MVO & Multi-Vense Optimizer [68] & 2016 \\ WOA & While Optimization Algorithm [69] & 2016 \\ SCA & Sine Cosine Algorithm [70] & 2016 \\ MFO & Mobi-Plame Optimization Algorithm [71] & 2015 \\ GWO & Grey Wolf Optimizer [72] & 2014 \\ PSO & Particle Swarm Optimization [73] & 1995 \\ SA & Simulated Annealing Algorithm [74] & 1983 \\ GA & Genetic Algorithm [75] & 1960 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Compared Optimization algorithms. functions (e.g., F7 and F10), where it effectively balances exploration and exploitation, enabling it to navigate diverse problem spaces and locate near-optimal solutions. The results of the CYO on the CEC2022 benchmark functions (F1-F12) are presented in Table 4, highlighting its superior optimization capabilities. The optimizer consistently ranks highly across various test functions, demonstrating robust performance across different problem landscapes. For example, in F1, the CYO achieves the lowest mean value of \(3.00\times 10^{2}\) and the smallest standard error (SEM) of \(1.78\times 10^{-3}\), securing the first rank.