Many optimization problems are inherently difficult and belong to a special class called NP-hard. Thus, efficient algorithms, which we shall call integrated languages or simply metaheuristics, are needed. These algorithms effectively search a solution space through the iterative application of intelligent search strategies. Some of the search strategies are borrowed from analogies with nature and social behavior of individuals, like ants, bees, and birds, while others are inspired by physical and chemical processes, as well as by those which are based on the procedure of inference and logic. Effectively, many problems are modeled as optimization problems. Examples include the employee scheduling problem, which aims at assigning needs that emerge in the company to its employees, so that all needs are met and costs are minimized; the vehicle routing problem, which aims at determining a set of routes and their sequence of stops to be executed by a fleet of vehicles, so that all customers receive their demands and the total cost is minimized; the quadratic assignment problem, which consists of allocating to m identical machines, n facilities, and computing the associated assignment, so that an objective function is minimized; and the maximum independent set problem, which consists of finding the largest set of nodes in a graph such that no two nodes in this set are adjacent and no adjacent nodes are present in the set. Additionally, optimization problems appear in different kinds of areas such as mathematics, physics, computer science, economics, and engineering.