## 5 Experimental Analysis of the Cybersecurity Optimizer
### Description of the CEC2022 Benchmark Functions F1 to F12
F4, the Shifted and Fully Rotated Non-Continuous Rastrigin's Function, is a non-differentiable function with numerous local minima, testing the robustness of algorithms under difficult conditions. Lastly, F5 is the Shifted and Fully Rotated Levy Function, which introduces abrupt changes in the landscape, rendering it highly deceptive and difficult to optimize. The Hybrid Functions F6 to F8 are hybrid benchmark functions, which combine multiple distinct optimization problems into a single, more complex function. Each Hybrid Function consists of components that differ in terms of difficulty, modality, and structure, presenting additional challenges to the optimization algorithms. F6, Hybrid Function 1 (N = 3), integrates three distinct sub-functions with varying characteristics, requiring the algorithm to adapt to different search landscapes within a single problem. F7, Hybrid Function 2 (N = 6), introduces six different components, necessitating greater flexibility from the algorithm to handle changes in modality and separability. Finally, F8, Hybrid Function 3 (N = 5), tests the algorithm's ability to strike a balance between exploration and exploitation across five distinct sub-problems within one optimization task. The composition functions F9 to F12 represent the most complex set of benchmark functions within this suite. Each composition function is constructed by combining several sub-functions into a single optimization problem, where each component is assigned a weight based on its level of difficulty. This design simulates real-world problems, where optimization landscapes can exhibit significant variation across different regions. F9, composition function 1 (N = 5), consists of five sub-functions with varying complexity and modality, creating a highly rugged search space with multiple global and local optima. F10, composition function 2 (N = 4), reduces the number of sub-functions but increases the complexity of interactions among them, testing the algorithm's adaptability. F11, composition function 3 (N = 5), incorporates five sub-functions with differing levels of separability, challenging the algorithm to address both separable and non-separable problems simultaneously. Finally, F12, composition function 4 (N = 6), presents the highest level of complexity with six distinct components, offering the most rigorous test of an algorithm's ability to optimize across diverse and challenging problem structures. The selection of these benchmark functions is essential for evaluating the robustness and generalization ability of optimization algorithms. By testing on unimodal, multimodal, hybrid, and composition functions, we can thoroughly assess an algorithm's capability to efficiently exploit search spaces in unimodal settings, explore highly complex multimodal landscapes with multiple local optima, adapt to sudden changes in problem characteristics within Hybrid Functions, and manage the extreme variability and complexity encountered in composition functions, which closely resemble real-world optimization challenges. These functions, thus, provide a balanced set of optimization tasks, allowing us to evaluate the strengths and limitations of the proposed algorithm under varying conditions, and ensuring its relevance and applicability across a broad spectrum of practical problems.