What is Ant Colony Optimization (ACO)

It is an optimization algorithm for combinatorial optimization problems inspired by the ants’ foraging behavior.

An algorithm, which is inspired 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.

A category of optimization algorithms, inspired from the real ant foraging behavior. ACO is a population-based metaheuristic which used to find approximate solutions to difficult optimization problems like travel sales man problem and dynamic routing problem that work on find optimal or near optimal paths to destinations.

Recently emerged optimization algorithm that is inspired by techniques used by swarms of ants. At each iteration of an ACO algorithm, each agent (ant) of the swarm builds a solution of the problem based on both it is own estimation and information the indirect form of communication (stigmergy) he attains with the other agents, through an artificial hormone (pheromone).

Is a class of optimization algorithms modeled on the actions of an ant colony. ACO methods are useful in problems that need to find paths to goals. Artificial 'ants' locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions.

One of the successful research directions in the SI domain. The main idea is that the self-organizing principles which allow a colony of artificial ants that cooperate to find good solutions.

It is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs by the ants.