Metaheuristic algorithms are advanced problem-solving techniques used to find approximate solutions for complex optimization problems. They are particularly useful when traditional methods are inefficient or infeasible. These algorithms often mimic natural processes, such as evolution or swarm behavior, to explore the solution space effectively. Common examples of metaheuristic algorithms include Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization. These methods are versatile and can be applied across various fields, including engineering, finance, and logistics, to tackle challenges like scheduling, resource allocation, and route optimization.