From a broader perspective, ACO performs a model-based search In the natural world, ants of some species (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails.If other ants find such a path, they are likely not to keep travelling at random, but instead to follow the trail, returning and reinforcing it if they eventually find food (see Ant communication).In the same fashion as the book, we use the berlin52 instance from TSPLIB as a testbed for the program.This program uses the Ant Colony System algorithm for solving the Travelling Salesman Problem.
Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph.
as a means of solving the travelling salesman problem (TSP).
AS draws an analogy between the optimization process and the foraging behaviour of real ants.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations.
TSP is a special case of the travelling purchaser problem and the vehicle routing problem.