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20-05-2017, 09:59 PM

hlo i am simer i would like to learn about ant colony optimization in matlab

20-05-2017, 10:01 PM

i wnt to learn bout ant colony optimizatiion

jaseela123d · 27-05-2017, 02:21 PM

Ant Colony Optimization (ACO) are a set of probabilistic meta-heuristics and an intelligent optimization algorithm, inspired by the social behavior of ants. ACO algorithms are also classified as Swarm intelligence methods, due to the application of this paradigm, through the simulation of ants behavior in the structure of these algorithms.

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

This algorithm is a member of the family of ant colony algorithms, in swarm intelligence methods, and constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his doctoral thesis, the first algorithm looked for an optimal path in a graph, based on the behavior of ants looking for a path between their colony and a food source. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have arisen, which are based on various aspects of ant behavior. From a broader perspective, ACO performs a model-based search and shares some similarities with the Distribution Algorithm Estimation.

In the natural world, the ants of some species (initially) roam at random, and upon finding the return of food to their colony while establishing the pheromone trails. If other ants find this path, they probably will not continue to travel randomly, but will follow the path, returning and reinforcing it if they finally find food (see Ant's communication).

Over time, however, the pheromone trail begins to evaporate, thus reducing its attractive strength. The longer it takes an ant to travel down the road and back again, the longer the pheromones have to evaporate. A short path, by comparison, is paraded more frequently, and therefore the density of pheromones becomes higher in trajectories shorter than the longer ones. Pheromone evaporation also has the advantage of avoiding convergence to a locally optimal solution. If there were no evaporation, the paths chosen by the first ants would tend to be excessively attractive to the next ones. In that case, the exploration of solution space would be limited. The influence of pheromone evaporation on real ant systems is unclear, but it is very important in artificial systems.

The overall result is that when an ant finds a good (ie short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads to all ants following a single path. The idea of the ant colony algorithm is to mimic this behavior with "simulated ants" by walking around the graph representing the problem to be solved.

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