Ant Colony Optimization

HYPerlink Cost
BasE Power

A collective endeavor to find the solution, be it in nature or science.

Illus. Simon Seene © 2022 Universität Innsbruck

Answers to FAQs

Discover more

Optimization problems can be incredibly challenging, demanding specialized algorithms like parallel optimization to provide effective solutions. In the animal kingdom, living beings face optimization tasks every day, striking a delicate balance between safety and risk. Take ants, for example, who excel at efficiently foraging for food. But here's the fascinating part: it is not a one ant job, but a team effort! Ants embark on their quest by initially exploring the environment at random, while leaving behind trails of pheromones that gradually fade away over time. When the distance to a food source is short, the concentration of pheromones is strong, increasing the likelihood of other ants following the trail. Inspired by this brilliant strategy, computer scientist Marco Dorigo developed the first version of the ant colony algorithm. Follow his journey into the world of nature’s algorithms, exploring the power of collective intelligence and its invaluable lessons for optimization.

Illus. Simon Seene © 2022 Universität Innsbruck

Search the archives

Learning from nature

How real-life problems can be solved by following ants.

This is some text inside of a div block.
All Cards
This is some text inside of a div block.