Special issue on ant colony optimization

Ant Colony Optimization (ACO) is one of the most successful techniques in the wider field of swarm intelligence. ACO is inspired by the pheromone trail laying and following behavior of some ant species, a behavior that was shown to allow real ant colonies to find shortest paths between their colony and food sources. Taking many elements of the real ants behavior, foremostly their indirect communication through pheromone trails, the first ACO algorithms were proposed more than 15 years ago. Since then ACO has attracted a large number of researchers. In the first few years of ACO research, the focus was mainly on algorithmic advancements, trying to make ACO algorithms competitive with established metaheuristic techniques. Currently, the majority of the contributions concern successful applications of ACO algorithms to a variety of challenging problems; still, another active and productive research area in ACO is the theoretical study of the behavior of specific ACO algorithms. The significant research efforts on ACO have established it as a mature metaheuristic that can lead to very effective algorithms for many difficult optimization problems. This special issue has the goal to collect papers on current, relevant work on ACO. We received 19 submissions on topics covering algorithmic developments, applications to continuous and combinatorial optimization problems, and theoretical studies. After a rigorous review process, three papers were selected for inclusion into this special issue. Among these, two