Implementation of an Ant Colony System to solve the Travelling Salesman Problem.
I used a modified version of the Ant Colony System (ACS). I started implementing that algorithm, and then I modified it to perform a 2-opt local search, followed by a 3-opt local search. Also, I choose to save the most promising solutions in a list. After the 20% first part of the run, these solutions are optimized using the local search techniques mentioned above. This allows to perform more exploration at first, and then, focus on improving the best solutions. These solutions are the only ones allowed to spread pheromone. Moreover, I implemented alpha so that it gradually decreases, using a truncated logarithmic function.
I implemented the algorithm using C++, and the highly-performant mathematics library Blaze. For the compilation, I delegated the task to CMake. I chose to precompute some parameters, such as the heuristics matrix that does not change in the evolution of the algorithm.
I used Bayesian Optimization to choose the hyperparameters for the algorithm.
To get my choice of hyperparameters, I used a Python script, included with my
code, that uses the bayesian_optimization library to perform repeated runs of the
algorithm, with the objective of finding the best choice of the hyperparameters
Average error (with respect to the optimum solution) of 3%.