Aco

Ant Colony Optimization (ACO) is a computational technique inspired by the way real ants find the shortest paths to food. It uses simulated "ants" (algorithm agents) that explore possible routes and leave behind virtual pheromones, which represent the quality of the paths. Over time, the path with the most pheromone becomes more attractive, guiding subsequent agents to better solutions. ACO is often used to solve complex problems like routing, scheduling, and optimization tasks, effectively leveraging collective behavior to find efficient solutions in large, intricate search spaces.