Metaheuristics are high-level, problem-independent algorithmic frameworks designed to efficiently explore large and complex search spaces to find near‑optimal solutions to hard optimization problems. They provide flexible templates—such as genetic algorithms, simulated annealing, tabu search, and particle swarm optimization—that can be adapted to many domains where exact methods are too slow or intractable. Metaheuristics matter because they enable practical solutions to NP-hard and real‑world optimization problems in engineering, logistics, AI/ML, and operations research.