Innovative Metaheuristic Algorithms for Reliability Optimization in Complex Systems: A Review
1Research Scholar, Department of Mathematics, Baba Mastnath University, Asthal Bohar, Rohtak .
2Research Supervisor, Department of Mathematics, Baba Mastnath University, Asthal Bohar, Rohtak
2Research Supervisor, Department of Mathematics, Baba Mastnath University, Asthal Bohar, Rohtak
Abstract
The optimization of reliability in complex engineered systems has become an urgent pursuit, as modern industries increasingly rely on highly integrated, large-scale systems whose failure can result in significant safety, operational, and economic consequences. Traditional optimization methods often struggle to cope with the nonlinear, high-dimensional, and multi-objective nature of reliability allocation and maintenance scheduling problems. In recent years, metaheuristic algorithms—such as Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Teaching-Learning-Based Optimization, and advanced hybrids—have emerged as powerful, flexible approaches for reliability optimization. This review surveys the evolution and recent breakthroughs in the field from 2010 to 2025, highlighting the development, application, and comparative performance of metaheuristic algorithms. Key advancements, challenges, and future research directions are discussed, with a focus on the practical impact of metaheuristics in industrial reliability engineering. The review aims to provide researchers and practitioners with a comprehensive reference for selecting and designing effective metaheuristic strategies to optimize complex system reliability.
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Keywords
Reliability optimization, Metaheuristic algorithms, Complex systems, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Hybrid metaheuristics, Redundancy allocation, System availability, Industrial reliability
Cite This Paper
Arun Kumar1, Prof. (Dr.) Jagatveer Singh2, "Innovative Metaheuristic Algorithms for Reliability Optimization in Complex Systems: A Review," in International Conference on Intelligent Engineering & Future Technologies (ICIEFT-2026), pp. 34-45.
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