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Metaheuristics are high-level problem-solving strategies that guide heuristic search processes to find approximate solutions for optimization problems. They are designed to be general enough to be applied to a wide range of problems, often when exact methods are too computationally expensive or impossible to implement. Metaheuristics do not guarantee finding the absolute optimal solution but aim to find a good enough solution within a reasonable amount of time.

The term "metaheuristic" itself suggests a heuristic above other heuristics. This means they often employ underlying heuristics or search mechanisms, but provide a framework for orchestrating these components effectively. Key characteristics of metaheuristics include their ability to escape local optima, explore a broad search space, and adapt to different problem structures.

Common categories of metaheuristics include evolutionary algorithms, such as genetic algorithms, which are inspired by natural selection; swarm intelligence algorithms, like ant colony optimization and particle swarm optimization, which mimic collective behaviors of social animals; and local search-based methods, such as simulated annealing and tabu search, which iteratively improve a solution by making small changes. The choice of a particular metaheuristic often depends on the specific characteristics of the problem being addressed, including its size, complexity, and the nature of the objective function.