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som_solver.py
Download provided codes and run the main script eval_som.py
eval_som.py
Slide 15 from the Lecture08:
Utilize 'alternate goal' concept for solving TSP with neighborhoods (TSPN). In each epoch, the neurons are adapted towards the goals which inhibits them. But, in the TSPN, the neurons are adapted to the closes point in the specific goal neighborhood. Therefore, this concept enables to find shorter solutions, see the right image and the following GIF with SOM evolution.
Click on the following image to see the SOM evolution in GIF.
select_winner(…)
learn_epoch(…)
update_goal_potition(…)
alternate goal