A Concise Overview of Applications of Ant Colony Optimization

Ant colony optimization (ACO) [1–3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic information. The pheromone trails are updated during algorithm execution to bias the ants search toward promising decisions previously found. The article titled Ant Colony Optimization gives a detailed overview of the main concepts of ACO. Despite being one of the youngest metaheuristics, the number of applications of ACO algorithms is very large. In principle, ACO can be applied to any combinatorial optimization problem for which some iterative solution construction mechanism can be conceived. Most applications of ACO deal with NP-hard combinatorial optimization problems, that is, with problems for which no polynomial time algorithms are known. ACO algorithms have also been extended to handle problems with multiple objectives, stochastic data, and dynamically changing problem information. There are extensions of the ACO metaheuristic for dealing with problems with continuous decision variables, as well. This article provides a concise overview of several noteworthy applications of ACO algorithms. This overview is necessarily incomplete because the number of currently available ACO applications goes into the hundreds. Our description of the applications