Ant colony optimization algorithms

Optimization algorithm This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages) This article may contain original research. Please improve it by verifying the claims made and adding inline citations. Statements consisting only of original research should be removed. (August 2018) (Learn how and when to remove this message) This article includes a list of general references but lacks sufficient corresponding inline citations. Please help improve this article by introducing more precise citations. (August 2018) (Learn how and when to remove this message) (Learn how and when to remove this message) Ant behavior was the inspiration for the metaheuristic optimization technique When a colony of ants is confronted with the choice of reaching their food via two different routes of which one is much shorter than the other, their choice is entirely random. However, those who use the shorter route reach the food faster and therefore go back and forth more often between the anthill and the food. In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic techn