metaheuristisch

Metaheuristisch is a term used in computer science and operations research to describe a broad class of algorithms designed to find approximate solutions to optimization problems. These problems are often computationally difficult, meaning that finding the exact optimal solution might take an unreasonable amount of time or resources. Metaheuristics do not guarantee finding the absolute best solution, but they aim to find a good enough solution within a practical timeframe.

The "meta" in metaheuristics refers to the idea that these algorithms operate at a higher level, guiding or orchestrating simpler heuristics. A heuristic is a problem-specific method that aims to find a good solution quickly. Metaheuristics provide a general framework that can be applied to a wide variety of optimization problems, often by combining or adapting basic heuristic strategies.

Common metaheuristic approaches include evolutionary algorithms (like genetic algorithms), simulated annealing, tabu search, and ant colony optimization. These methods are inspired by natural processes or human behavior and employ mechanisms such as randomness, iteration, and exploration of the solution space to escape local optima and converge towards better solutions. The effectiveness of a metaheuristic often depends on its tuning and how well it is adapted to the specific characteristics of the problem being solved.