Where can I learn more about “ant colony” optimizations?

Question

I\'ve been reading things here and there for a while now about using an \"ant colony\" model as a heuristic approach to optimizing various types of algorithms. However, I have

Answer 1

link Wikipedia actually got me started. I read the article and got to coding. I was solving a wicked variation of the traveling salesman problem. It's an amazing meta-heuristic. Basically, any type of search problem that can be put into a graph (nodes & edges, symmetric or not) can be solved with an ACO.

Look out for the difference between global and local pheromone trails. Local pheromones discourage one generation of ants from traversing the same path. They keep the model from converging. Global pheromones are attractors and should snag at least one ant per generation. They encourage optimum paths over several generations.

The best suggestion I have, is simply to play with the algorithm. Setup a basic TSP solver and some basic colony visualization. Then have some fun. Working with ants, conceptually, is way cool. You program their basic behaviors and then set them loose. I even grow fond of them. :)

ACOs are a greedier form of genetic algorithms. Play with them. Alter their communicative behaviors and pack behavior. You'll rapidly begin to see network / graph programming in an entirely different way. That's their biggest benefit, not the recipe that most people see it as.

You just gotta play with it to really understand it. Books & research papers only give a general sky-high understanding. Like a bike, you just gotta start riding. :)

ACOs, by far, are my favorite abstraction for graph problems.