This course is run at the The University of Nottingham within the School of Computer Science & IT. The course is run by Graham Kendall (EMAIL : gxk@cs.nott.ac.uk)
Imagine that you are an ant. You are practically blind, which is a bit of a
problem when you are trying to find your way from your home to a source of food
or trying to find your way from some food back to your home.
But, we know that ants find their way to and from food all the time. So how do they do it?
It was these type of observations and questions that inspired a new type of
algorithm called ant algorithms (or ant systems).
These algorithms are very new (Dorigo, 1996) and is still very much a research area. It might be the case that they turn out not to produce the results that the initial research suggests they are capable of, but the initial results are promising.
The ant system is a population based approach. In this respect it is similar to genetic algorithms although there is not a population of solutions being maintained. Rather, there is a population of ants, with each ant finding a solution and then communicating with the other ants in the hope it will help them find even better solutions.
As part of this section of the course it is recommended that you take a look at the ant optimization home page ( http://iridia.ulb.ac.be/~mdorigo/ACO/ACO.html).
You should also read The Ant System: Optimization by a Colony of Cooperating Agents (Dorigo M., V. Maniezzo & A. Colorni (1996). The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1):29-41), which was the first ant paper and the one that most of this material is based on.
An alternative paper (by the same author) is Ant Colonies for the Traveling Salesman Problem (Dorigo M. & L.M. Gambardella (1997). Ant Colonies for the Traveling Salesman Problem. BioSystems, 43:73-81. (Also Tecnical Report TR/IRIDIA/1996-3, IRIDIA, Université Libre de Bruxelles.)
Both these papers are available on the ant colony web site but I have provided local access to them, in order to make access easier for you.
I also recommend that you access other resources on the internet, as there is lots of information about ant algorithms.
Try this Google Search, for starters
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Last Updated : 26/01/2002