Ant Colony Optimization Algorithm
Jump to navigation
Jump to search
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 ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
- ↑ 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.