Metaheuristics are advanced problem-solving techniques used to find good enough solutions for complex optimization problems. Unlike traditional methods that aim for the best solution, metaheuristics explore various possibilities and trade-offs, making them suitable for problems where finding the perfect answer is impractical or time-consuming. They are often inspired by natural processes, such as evolution or swarm behavior. These algorithms, such as Genetic Algorithms and Simulated Annealing, work by iteratively improving candidate solutions. They balance exploration of new areas in the solution space with exploitation of known good solutions, allowing them to efficiently navigate through large and complicated problem landscapes.