A Concise Overview of Applications of Ant Colony Optimization
Thomas Stützle, Manuel López‐Ibáñez, Marco Dorigo · Wiley Encyclopedia of Operations Research and Management Science · 2011
Abstract Ant colony optimization (ACO) is a metaheuristic for solving hard combinatorial (discrete) optimization problems; it is inspired by the foraging behavior of real ants. Despite being a rather recent technique, the number of applications of ACO algorithms is very large. 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, as well, extensions of the ACO metaheuristic for dealing with problems with continuous decision variables. This chapter provides a concise overview of the most 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 extends with many recent examples the list proposed in 2004 by Dorigo and Stützle in their book on ACO.