A strong interest in recent years appeared on procedures for the solution of NP-hard combinatorial optimization problems which are inspired by natural courses of events. One of the most recent one of these nature analogous procedures is the optimization with ant colonies (ACO, Ant Colony Optimization). This is inspired by behavior of real ant colonies during forage. It represents the experiment as in the case of the genetic algorithms to solve optimization problems heuristically by adaptation of natural behavior. But all procedures, which use an heuristic basis can return a statement about the solution quality.
Nobody know precisely why the ants are ecologically so successful. Since 135 million years, this species hardly underwent changes in the evolution. The key could be the swarm intelligence how it also can be found at other insect states.
An ant can put pheromons in any thickness and mark its' trail this way. While isolated ants almost move by chance, an ant meeting a pheromone lane can observe these and follows it. The ant itself can put pheromons simultaneously again, strengthening the trace. In such a way, a positive reaction bow results: the more ants follow a path, the more attractive it becomes for the entire colony.
Pheromones evaporate with time similar to other perfumes. Therefore, unused paths loose their attraction potential as time passes by.