# Ant Colony Optimization Algorithm

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An Ant Colony Optimization Algorithm is a probabilistic optimization algorithm based on graph path identification.

**AKA:**ACO.**See:**Computer Science, Operations Research, Swarm Intelligence, Metaheuristic, Ant Colony.

## References

### 2015

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms Retrieved:2015-1-12.
- 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. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,^{[1]}^{[2]}the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants.

- In computer science and operations research, the

- ↑ A. Colorni, M. Dorigo et V. Maniezzo,
*Distributed Optimization by Ant Colonies*, actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991. - ↑ M. Dorigo,
*Optimization, Learning and Natural Algorithms*, PhD thesis, Politecnico di Milano, Italy, 1992.