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Ant colony optimization|Bayesian and credal network|Genetic algorithms|Tabu search|Simulation
Ant colony optimization

The ant colony optimization (ACO) metaheuristic is a population-based approach to the solution of combinatorial optimization problems. The basic ACO idea is that a large number of simple artificial agents are able to build good solutions to hard combinatorial optimization problems via low-level based communications. Real ants cooperate in their search for food by depositing chemical traces (pheromones) on the ground. An artificial ant colony simulates this behavior. Artificial ants cooperate by using a common memory that corresponds to the pheromone deposited by real ants. The artificial pheromone is accumulated at run-time through a learning mechanism. Artificial ants are implemented as parallel processes whose role is to build problem solutions using a constructive procedure driven by a combination of artificial pheromone, problem data and a heuristic function used to evaluate successive constructive steps. Recently, several ACO based algorithms that can be applied to a wide class of combinatorial optimization problems have been studied and developed. In some of these domains, such as the quadratic assignment problem, the sequential ordering problem, the vehicle routing with time window, and telecommunication routing, the developed algorithms are among the best currently worldwide available, and for many benchmark instances new best known solutions have been computed.

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