Ants navigate using indirect communication through pheromones — chemical signals deposited in the environment. When a forager finds food, it returns to the nest while secreting a trail pheromone. Subsequent ants preferentially follow stronger trails (which are shorter paths, since pheromones on them are refreshed more often before evaporating). This positive feedback loop spontaneously discovers shortest paths — the basis of Ant Colony Optimization (ACO) algorithms.
The colony as a superorganism exhibits collective intelligence that no individual ant possesses. This is stigmergy: coordination through environmental modification. The same principle governs termite mound construction, bee waggle dances, and slime mold network formation.
ACO algorithm (Dorigo 1992) has been successfully applied to the Travelling Salesman Problem, network routing, and protein folding — showing that biology's solutions predate computer science's rediscovery of them.