Review of the Metaheuristic Algorithms in Operations Research
Abstract
Metaheuristic algorithms have gained significant attention in operations research due to their ability to solve complex optimization problems efficiently. These algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Tabu Search (TS), offer flexible and robust solutions for various real-world applications, such as scheduling, logistics, and resource allocation. This review provides a comprehensive analysis of different metaheuristic approaches, highlighting their strengths, limitations, and practical applications. Furthermore, a comparative study of recent literature is conducted to evaluate algorithmic performance, hybridization techniques, and emerging trends in the field. The study also discusses future research directions, emphasizing the need for adaptive, hybrid, and machine learning-integrated metaheuristics to enhance solution quality and computational efficiency.
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