Metaheuristics is a class of computational optimization algorithms or procedures designed to find high-quality, near-optimal solutions for complex problems where exact methods are computationally prohibitive or infeasible. This academic concept investigates computationally hard optimization problems characterized by vast, intricate, or discrete search spaces. Key characteristics include their general-purpose nature, adaptability across diverse problem domains, reliance on heuristic or stochastic strategies, and the inherent balance between exploring the search space and exploiting promising regions to achieve practical solutions. Their significance lies in providing effective and efficient approaches for tackling many real-world optimization challenges that are otherwise intractable, making them fundamental tools in numerous scientific, engineering, and operational disciplines.