The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances

Ant Colony Optimization (ACO) [31, 32] is a recently proposed metaheuristic approach for solving hard combinatorial optimization problems. The inspiring source of ACO is the pheromone trail laying and following behavior of real ants which use pheromones as a communication medium. In analogy to the biological example, ACO is based on the indirect communication of a colony of simple agents, called (artificial) ants, mediated by (artificial) pheromone trails. The pheromone trails in ACO serve as a distributed, numerical information which the ants use to probabilistically construct solutions to the problem being solved and which the ants adapt during the algorithm’s execution to reflect their search experience. The first example of such an algorithm is Ant System (AS) [29, 36, 37, 38], which was proposed using as example application the well known Traveling Salesman Problem (TSP) [58, 74]. Despite encouraging initial results, AS could not compete with state-of-the-art algorithms for the TSP. Nevertheless, it had the important role of stimulating further research on algorithmic variants which obtain much better computational performance, as well as on applications to a large variety of different problems. In fact, there exists now a considerable amount of applications obtaining world class performance on problems like the quadratic assignment, vehicle routing, sequential ordering, scheduling, routing in Internet-like networks, and so on [21, 25, 44, 45, 66, 83]. Motivated by this success, the ACO metaheuristic has been proposed [31, 32] as a common framework for the existing