The ant colony optimization algorithm (ACO), introduced by Moyson and Manderick and widely developed by Marco Dorigo [CMD91,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.
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 (Details on this behaviour.)
More on [ Ant colony optimization ]
Algorithms :: Computers
Operations Research :: Math

Ant Colony Optimization - Optimization methodology based on ant behaviors. The first ACO system was introduced by Marco Dorigo in 1992. ACO was applied to the travelling salesman problem, and to the quadratic assignment problem.
ANTS Workshop Series - From Ant Colonies to Artificial Ants: A Series of International Workshops on Ant Algorithms. Links to individual conferences and proceedings.
Saurabh Samdani - Source codes - Site contains ant colony optimization software with source code in c / c++ for engineering optimization problems.
Meta Description: [ Saurabh Samdani's Homepage ]
Simulation of Ant's Emergent Behavior Using StarLogo - A virtual ant colony was created to investigate the emergent behaviors demonstrated by ants. It uses the StarLogo simulation tool.
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