ABSTRACT
This chapter presents an overview of ant colony optimization (ACO)—a metaheuristic inspired by the behavior of real ants. ACO was proposed by Dorigo et al. as a method for solving hard combinatorial optimization problems (COPs). ACO was inspired by the observation of the behavior of real ants. One of the first researchers to investigate the social behavior of insects was the French entomologist Pierre-Paul Grasse. In the 40s and 50s of the twentieth century, he was observing the behavior of termites—in particular, the Bellicositermes natalensis and Cubitermes species. He discovered that these insects are capable to react to what he called “significant stimuli,” signals that activate a genetically encoded reaction. ACO has been formalized into a combinatorial optimization metaheuristic by Dorigo et al. and has since been used to tackle many COPs. Given a COP, the first step for the application of ACO to its solution consists in defining an adequate model.
