AICup/approach.md

Approach used to solve the AI cup

My submission for the AI cup uses a Ant Colony Optimization algorithm implementation paired with a 3-opt optimizer.

For efficiency's sake, both the algorithm and the optimizer were implemented in C++. However, solution checking and the calculation of the euclid distance matrix are still computed in a modified version of the Python 3 code of the AI2020BsC repository from Mr. Montegazza.

The Python code first compiles the C++ portion of the code in an executable. Then, a Python wrapper is used to call said executable. The distance matrix is computed by the Python implementation and then saved in a .txt file. The C++ executable reads said file and, after executing the algorithm and the optimizer, the program writes on the standard output a python expression containing the solution array. This expression is then read by the Python wrapper and evaluated using eval(...), and then is is checked and displayed thanks to the original AI2020BsC code.

More details on how to compile and run the program can be found in execute.md.

The C++ implementation is by default non-parallel, as I interpreted the "Single CPU" restriction described in the cup introductory PDF as meaning that the code must run on a single core. The implementation can however be easily converted in a multi-core one ant per thread implentation by changing the #define SINGLE_CORE flag in aco.cc to 0. Note that the 3 minute limit is of course never reached even when using the single core implementation.