The Repositry PA-Net is in support of the work:
"Pareto Frontier Approximation Network (PA-Net) Applied to Multi-objective TSP"


PA-Net uses the architecture of network presented in :

Learning Heuristics for the TSP by Policy Gradient, Deudon M., Cournut P., Lacoste A., Adulyasak Y. and Rousseau L.M.


The code of PA-Net is modified version of the code by Deudon et. el :
github link : https://github.com/MichelDeudon/encode-attend-navigate



In this repo user can visualize Pareto Fronts generated by PA-Net for 2-objective MOTSP for different city sizes.

The requirements to run the code are:
-Python 3.6
-Tensorflow 1.15
-sklearn
-matplotlib


To run the code for  200 city MOTSP,  Go into the PANet directory  and run the following command:

python PA_net_test.py -ct 200

The output will be displayed as a plot and the values of objective functions will be saved in .csv file

To train your own network run:

python PA_net_train.py 
