In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs. Artificial ants represent multi …

2020年5月17日 · Ant Colony Optimization technique is purely inspired from the foraging behaviour of ant colonies, first introduced by Marco Dorigo in the 1990s. Ants are eusocial insects that prefer community survival and sustaining rather than as individual species.

Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony.

Searching for optimal path in the graph based on behaviour of ants seeking a path between their colony and source of food. Ants navigate from nest to food source. Ants are blind! Shortest path is discovered via pheromone trails.

2006年12月1日 · Ant colony optimization exploits a similar mechanism for solving optimization problems. From the early nineties, when the first ant colony optimization algorithm was proposed, ACO...

An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications.

2024年12月1日 · Ant colony optimization (ACO) is a nature-inspired metaheuristic for solving hard optimization problems. In particular, ACO was inspired by the ability of natural ant colonies to find short paths between their nest and food sources.