AICup/approach.md

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# 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.