EVALUATING THE IMPACT OF PARAMETER SENSITIVITY IN META-HEURISTICS ON RELIABILITY PERFORMANCE

The optimizing reliability of a system is a crucial target for any industry to have good values and outcomes with the balanced cost of maintenance. The assessment of costs and reliability in intricate systems, such as Distributed Redundancy Architecture (DRA), is essential for optimal system design. Achieving this involves addressing various constraints to enhance the overall system's reliability. Researchers have explored system dependability and cost optimization challenges, emphasizing the evolution of metaheuristic approaches. Enhancing system resilience and optimizing costs are central objectives of this study, aiming to improve the reliability and cost-efficiency of DRA systems. The study's framework builds upon existing metaheuristic methods, including Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Gazelle Optimization Algorithm (GOA), and Coati Optimization Algorithm (COA). The findings reveal that COA demonstrates superior performance compared to other methods. By employing the Coati Optimization Algorithm, this study effectively addresses cost and reliability-optimization challenges, providing highly efficient solutions. Key metrics such as reliability and cost were compared across the five metaheuristic approaches, showcasing COA’s advantages. Notably, the Coati Optimization Algorithm delivers faster resolutions and outperforms its counterparts in reducing costs while enhancing system reliability. The results underscore COA's ability to optimize intricate system parameters more effectively than methods like MFO, WOA, GOA, and DA. This research highlights the value of using COA for improving reliability and minimizing expenses in complex systems. It offers a novel approach to addressing these challenges, establishing COA as a powerful tool for optimizing distributed system architectures. By analyzing comparative solutions, the study found that COA consistently achieved better outcomes, affirming its success in reliability and cost optimization. In conclusion, this research emphasizes the importance of advanced metaheuristic techniques, particularly the Coati Optimization Algorithm, in enhancing the performance of Distributed Redundancy Architectures. By presenting a numerical example, the comparison has been made and the outcomes are shown graphically and in the tabular form to have a clear understanding. The findings contribute significantly to the field by providing a robust method for achieving costefficient and reliable system designs.