Metaheurisztikus

Metaheurisztikus is a term used in computer science and operations research to describe a high-level strategy or a set of rules that guides an underlying heuristic search process. Unlike a specific heuristic, which is designed to solve a particular problem, a metaheurisztikus is more general and can be adapted to a wide range of optimization problems. The goal of a metaheurisztikus is to explore the search space effectively, trying to find a good enough solution within a reasonable amount of time, especially for complex problems where finding the absolute optimal solution is computationally infeasible.

Metaheurisztikus methods typically involve a mechanism to escape local optima, which are solutions that are better than their immediate neighbors but not necessarily the global best. They often combine exploration (searching new areas of the solution space) and exploitation (intensifying the search in promising regions). Examples of well-known metaheurisztikus include simulated annealing, genetic algorithms, tabu search, and ant colony optimization. Each of these has its own underlying principles for guiding the search, but they all share the characteristic of being a framework for designing problem-specific heuristics. They do not guarantee optimality but are widely used for their ability to find high-quality solutions for challenging optimization tasks.