Ant Colony Optimization (ACO) is a computational algorithm inspired by the foraging behavior of ants. It simulates how ants find the shortest paths to food sources by laying down pheromones, which guide other ants. Over time, paths with stronger pheromone trails are favored, leading to efficient route discovery. This optimization technique is widely used in solving complex problems, such as routing, scheduling, and network design. ACO is particularly effective for problems where multiple solutions exist, as it explores various paths and converges on the best one through collective learning and adaptation.