## 3 Related Work and Applications of RSO  
The RSO has been significantly effective in the coordinated design of power system stabilizers (PSSs) and static VAR compensators (SVCs). The adaptive rat swarm optimization (ARSO) variant incorporates the concept of the opposite number to enhance the initial population and employs opposite or random solutions to avoid local optima. This approach considerably improves global search capabilities. The performance of the ARSO variant has been validated against standard benchmarks and in case studies, proving its superiority in achieving optimal damping and outperforming traditional methods [11].  
In the realm of photovoltaic (PV) systems, ARSO combined with pattern search (PS) has shown outstanding performance. This hybrid method leverages the global search ability of ARSO and the local search strength of PS, resulting in high accuracy, reliability, and convergence speeds in extracting parameters from single-diode, double-diode, and PV modules [14]. Similarly, the RSO has been adapted for feature selection (FS) in various domains, including Arabic sentiment analysis and cybersecurity. Enhancements like binary encoding, opposite-based learning strategies, and the integration of local search paradigms from particle swarm optimization (PSO) have been introduced. These modifications improve local exploitation, diversity, and convergence rates, leading to superior performance compared to other FS methods [9; 15; 16].  
The RSO's versatility extends to healthcare, particularly in diagnosing COVID-19. The hybrid RSO-AlexNet-COVID-19 approach combines the RSO with convolutional neural networks (CNNs) to optimize hyperparameters and enhance diagnostic accuracy. This method achieved 100% classification accuracy for CT images and 95.58% for X-ray images, surpassing other CNN architectures [12]. Additionally, the modified RSO algorithm has been applied to data clustering, enhancing the accuracy and stability of clustering results. By integrating nonlinear convergence factors and reverse initial population strategies, this method addresses the limitations of traditional clustering algorithms like K-means. Moreover, the modified particle swarm optimization rat search algorithm (PSORSA) has improved PV system modeling, showcasing superior accuracy and efficiency in parameter extraction [17; 18].  
The RSO's adaptability is also evident in solving the NP-hard Traveling Salesman Problem (TSP). By incorporating decision-making mechanisms and local search heuristics like 2-opt and 3-opt, the hybrid HDRSO algorithm demonstrated robust performance on symmetric TSP instances, achieving competitive results [19; 20]. In manufacturing, the RSO has been employed to address flow shop scheduling problems. By mapping rat locations to task-processing sequences, the RSO optimizes execution time and resource use, enhancing production system flexibility, lead times, and quality [21]. Furthermore, the Modified Rat Swarm Optimization with Deep Learning (MRSODL) model combines the RSO with deep belief networks (DBNs) for robust object detection and classification in waste recycling. The ERSO-DSEL model similarly leverages feature selection and ensemble learning to enhance cybersecurity, achieving high accuracy in intrusion detection [15; 22].  
Lastly, the RSO has been applied to load frequency control (LFC) in power systems, improving controller performance and stability. The RSO-based Proportional-Integral-Derivative (PID) controller has demonstrated superior results compared to traditional methods, reducing frequency errors and the settling time [23].