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The ant colony optimization algorithm (ACO), introduced by Marco Dorigo Dor92,DoSt04, is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. They are inspired by the behaviour of ants in finding paths from the colony to food.
Overview In the real world, ants (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 to instead follow the trail, returning and reinforcing it if they eventually find food (see Ant communication and behavior). Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over faster, and thus the pheromone density remains high as it is laid on the path as fast as it can evaporate. Pheromone evaporation has also the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the paths chosen by the first ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution space would be constrained. Thus, when one ant finds a good (short, in other words) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leaves all the ants following a single path. The idea of the ant colony algorithm is to mimic this behavior with "simulated ants" walking around the graph representing the problem to solve. Ant colony optimization algorithms have been used to produce near-optimal solutions to the travelling salesman problem. They have an advantage over simulated annealing and genetic algorithm approaches when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in network routing and urban transportation systems. There are applications of ACO to machine learning and data mining problems, as well. For instance, one modification of the basic ACO metaheuristic which has been studied is to create a model of cemetery maintenance wherein worker ants "cluster" ant corpses. This has been adapted to the unsupervised machine learning task referred to as clustering wherein one wishes to find groups of objects that are "similar." In fact such modified forms of ACO have been shown to give better performance and accuracy than various classical methods such as the well-known k-means algorithm. Related methods Simulated Annealing (SA) is a related global optimization technique which traverses the search space by generating neighbouring solutions of the current solution. A superior neighbour is always accepted. An inferior neighbour is accepted probabilistically based on the difference in quality and a temperature parameter. The temperature parameter is modified as the algorithm progresses to alter the nature of the search. Tabu search (TS) is similar to Simulated Annealing, in that both traverse the solution space by testing mutations of an individual solution. While simulated annealing generates only one mutated solution, tabu search generates many mutated solutions and moves to the solution with the lowest fitness of those generated. In order to prevent cycling and encourage greater movement through the solution space, a tabu list is maintained of partial or complete solutions. It is forbidden to move to a solution that contains elements of the tabu list, which is updated as the solution traverses the solution space. Genetic Algorithms (GA) maintain a pool of solutions rather than just one. The process of finding superior solutions mimics that of evolution, with solutions being combined or mutated to alter the pool of solutions, with solutions of inferior quality being discarded. See also Publications (selected) an Example of Self-Organization in Massive Parallelism, Proceedings of AAAI Spring Symposium on Parallel Models of Intelligence, Stanford, California, 1988. | ||||||||
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