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July 08, 2011 | By:  Paige Brown
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Of Biomimicry and Learning From Ants

Biomimicry, a term originating from the word roots bios (life), and mimesis (to imitate), describes the imitation of systems and processes in nature that inspire solutions to human problems. Biomimicry describes not so much the use of naturally occurring systems to solve our problems, for example the use of bacteria to clean waste-water in water-treatment technologies, but rather the design of solutions and new technologies as inspired by nature, also known as bio-inspired design. Such bio-inspired design takes the form of robots that can climb walls as inspired by Gecko feet, stain-resistant fabrics and water/ice-free aircraft parts1 as inspired by waxy ‘nano’-bump-covered lotus leaves, and insect-inspired robots that can work together similar to an insect ‘swarm’, with seemingly ‘intelligent’ results (see a video here).

‘Learning about the natural world is one thing… Learning from the natural world, that is the profound switch.’ – Janine Benyus in 12 Sustainable Design Ideas From Nature.

Nature inspires new ways to solve complex problems through computational models2. Although many examples of biomimicry exist, one striking case study is the design of path-finding and optimization computer technologies inspired by naturally occurring ant colony behavior. In the words of Len Fisher, author of “The Perfect Swarm”, scientists have discovered that ants find the shortest route to a food source “not by looking at their watches to check the time but by using chemical signaling compounds, pheromones, which they lay down as they travel so that other ants can follow their trail.” The first ants to choose the shortest path to a food source, or who happen to find a shortcut, return to the nest before their fellow food-seekers. These on-their-ballgame ants leave strong chemical signals along their chosen path, allowing their companions back at the nest to preferentially follow the shortest route to the scrumptious food source themselves. Additionally helping the ant to more quickly locate his food source and to conserve energy, the chemical signal pheromones evaporate over time, meaning that longer trails become increasingly less attractive than shorter routes as pheromones evaporate to a greater extent along these longer routes3 (like footprints in a snowstorm). Communication between the ants, mediated by ‘smelly’ chemicals, is paramount in producing the ‘intelligent’ movements of the colony to and from a food source.

Computer scientists have exploited the ways of the ant to produce computational algorithms better able to solve complex problems. For example, computerized virtual ants can be used to find the shortest route between sixteen different cities, a problem that otherwise requires the evaluation of 653,837,184,000 different routes4 by ‘brute-force’ calculation. Ant colony optimization is a technique for solving hard computational problems, which are simplified such that their solutions involve simply finding the best path through a problem ‘map’, just as ants find the best path through a crack in your wall, under the dishwasher, and up onto your counter. For example, ant colony optimization can be used to find the shortest route between the 16 different European cities you’d like to visit this summer. This is also known as the traveling salesman problem, which can become very complex and difficult to solve by ‘brute force’ computation as the number of locations to visit goes up. By sending a bunch of virtual ‘ants’ into the problem set, an optimized solution can be found quickly and easily through positive reinforcement of the ‘best’ or shortest route. This positive reinforcement occurs as the virtual ants leave signals for each other along the best routes, just as real ants leave chemical odors for their fellow ants to ‘smell’ as they travel along shortcuts they find between their nest and that piece of cupcake you left out this morning.

Ant colony optimization has many applications in the real world. For example, virtual ants have help to improve vehicle and communications network routing. Many mail delivery and gasoline truck companies are already using ant colony optimization to improve their routes from stop to stop. Similar concepts have been used in communications networks, where ant colony behavior has inspired better distribution of media through networks and to mobile users5 (like those of you reading this blog post on your iPhone or iPad!)

Ant colony optimization is just one example out of many instances where nature inspires better solutions to our everyday problems. Join us over the next month as Student Voices bloggers investigate other interesting and exciting cases of biomimicry! Follow us online at @ScitableBlogs.


1. Prachi Patel. Energy: Water-Repelling Metals. Technology Review Published by MIT (2008)

2. E. Bonabeau, M. Dorigo & G. Theraulaz. Inspiration for optimization from social insect behavior. Nature 406, 39-42 (6 July 2000)

3. Artificial intelligence: Riders on a swarm. The Economist (April 2010)

4. The Perfect Swarm: The Science of Complexity in Everyday Life, Len Fisher, Ph.D. Basic Books 2009

5. G. Di Caro, M. Dorigo. AntNet: Distributed Stigmergetic Control for Communications Networks. Journal Of Artificial Intelligence Research 9, 317-365 (1998)

Bonabeau E, Dorigo M, & Theraulaz G (2000). Inspiration for optimization from social insect behaviour. Nature, 406 (6791), 39-42 PMID: 10894532

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