One major issue is its tendency to converge too quickly, often before adequately exploring the search space, leading to suboptimal solutions. This premature convergence is particularly problematic in complex problems. Additionally, the RSO struggles to maintain a proper balance between exploitation (local search) and exploration (global search), which reduces its effectiveness in navigating multimodal landscapes. It is also prone to getting trapped in local optima, especially in fixed-dimension multimodal problems, limiting its ability to reach the global optimum. Furthermore, the RSO's performance declines when dealing with high-dimensional tasks, raising concerns about its scalability. Lastly, the absence of comprehensive benchmarking across diverse problems limits the validation of the RSO's reliability and versatility in different scenarios. To overcome these limitations, this research introduces the Modified Rat Swarm Optimizer (MRSO), designed to enhance the performance and robustness of the original RSO in complex optimization tasks. The MRSO aims to achieve a better balance between exploration (global search) and exploitation (local search), reducing the risk of premature convergence and preventing the algorithm from getting trapped in local optima. By improving the exploration of the search space, the MRSO provides more accurate and reliable solutions, particularly in high-dimensional and multimodal problems. Additionally, the MRSO incorporates mechanisms that enhance scalability and robustness in various environments, making it more effective for a wide range of optimization challenges, includingreal-world engineering problems. The main objective of the MRSO is to deliver faster, more consistent convergence while improving versatility and efficiency across various optimization scenarios. The following are the main contributions of this modification:  
* Development of MRSO: The proposed algorithm enhances the RSO by introducing mechanisms to better balance exploration and exploitation, thereby improving its overall search capabilities. * Application to Engineering Problems: The MRSO is applied to seven real-world constrained engineering problems, demonstrating its effectiveness in addressing these complex challenges. * Comprehensive Benchmarking: The MRSO's performance is rigorously tested against classical benchmark functions, CEC 2019 test functions, and engineering problems, outperforming the RSO and showing competitive results compared to other optimization algorithms. * Exploration and Exploitation Analysis: A detailed analysis of the MRSO's ability to balance exploration and exploitation is presented, highlighting its effectiveness in avoiding local optima and achieving global solutions. * Limitations and Future Work: The paper acknowledges the limitations of the MRSO and suggests future research directions, including testing on large-scale real-world problems and incorporating surrogate models to handle computationally expensive tasks. The structure of this paper is organized as follows: Section 2 provides the theoretical background of the Rat Swarm Optimizer (RSO), offering a detailed explanation of its principles. Section 3 explores the applications of the RSO algorithm and reviews related works, highlighting its effectiveness across various fields. Section 4 presents an in-depth discussion of the proposed Modified Rat Swarm Optimizer (MRSO) algorithm. Section 5 focuses on benchmark testing and performance analysis of the MRSO, comparing it with the standard RSO. Section 6 examines the application of the MRSO to engineering design problems, showcasing its practical utility. Finally, Section 7 draws conclusions from the study, highlights the challenges encountered, and suggests areas for future research efforts.