Ant colonies, and more generally social
insect societies, are distributed systems that, in spite of the simplicity of
their individuals, present a highly structured social organization. As a result
of this organization, ant colonies can accomplish complex tasks that in some
cases far exceed the individual capabilities of a single ant. The field of ‘‘ant
algorithms’’ studies models derived from the observation of real ants’
behavior, and uses these models as a source of inspiration for the design of novel
algorithms for the solution of optimization and distributed control problems. The
main idea is that the self-organizing principles which allow the highly
coordinated behavior of real ants can be exploited to coordinate populations of
artificial agents that collaborate to solve computational problems. Several
different aspects of the behavior of ant colonies have inspired different kinds
of ant algorithms. Examples are foraging, division of labor, brood sorting, and
cooperative transport. In all these examples, ants coordinate their activities
via stigmergy, a form of indirect communication mediated by modifications of
the environment. For example, a foraging ant deposits a chemical on the ground
which increases the probability that other ants will follow the same path.
Biologists have shown that many colony-level behaviors observed in social
insects can be explained via rather simple models in which only stigmergic
communication is present. In other words, biologists have shown that it is
often su‰cient to consider stigmergic, indirect communication to explain how
social insects can achieve self-organization. The idea behind ant algorithms is
then to use a form of artificial stigmergy to coordinate societies of
artificial agents.
This technique of ant communication for
finding the shortest available path between their nest and the food source by
depositing pheromones can be applied for optimization various engineering
problems and there comparison with other conventional techniques.
