What is the Ant Colony Optimization Algorithm?

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In an internet-based society, online trade is consistently becoming more important. However, an increasing number of parcels also means an increase in logistical complexity.

Let’s assume a German parcel delivery service has to deliver 40 parcels to different addresses all over Germany. To save time, the fastest route between the cities has to be found.

If a computer were to calculate each individual route – from Frankfurt to Düsseldorf to Berlin as well as from Berlin to Frankfurt to Düsseldorf and so on – we would end up with 815 quattuordecillion possibilities. That’s an 8 with 47 zeros. And should one of the cities change the next day, the routes have to be computed anew all over again. Even modern supercomputers cannot calculate such amounts.

The ant colony optimization algorithm helps to find a solution to this. With a simple mathematical procedure, it simulates the routes in a way that is used by ant colonies to find the best route while foraging for food: An ant that has discovered a short and efficient route will walk to and from the food source more frequently than ants that walk a longer route. The colony notices this by the pheromones emitted by the faster ant, and so can identify the quickest route without having to try each one.

With the ant colony optimization algorithm, the computer learns how to think like an ant colony and can calculate the fastest route much quicker. This helps the parcel delivery service to save on both time, and fuel.