Ant Colony Optimization for TSP problems
This repository contains an implementation for solving TSP problems with the famous meta-heuristics ACO (Ant Colony Optimization). It runs several agents (Ants) through a weighted random walk until it converges to a (hopefully) good minimum.
This features a fully multi-threaded (and lock-free) implementation of ACO and contains a GridSearch to optimize the hyper parameters for a problem. The repository includes the Berlin52 problem, but can be adapted to virtually every TSP file from the University of Heidelberg's TSP group.
The throughput on my machine (i7-5820k @ 3.3ghz and ddr4 RAM) is roughly 13 agents per millisecond (or ~1 agent per logical core per millisecond) and it scales linearly with more cores.
For more details, consult Wikipedia or my blog post from 2011.
Initially this was a prototype for a paper in Software Engineering (second semester undergraduation). Since Google Code closed it's gates, I decided to rewrite major parts and port it to Java 8- although for the most part, it should work with Java 6 without major modifications.
Build
To build locally, you will need at least Java 8 to build this library.
You can simply build with:
mvn clean package install
The created jars contains debuggable code + sources.