udpate

run.py

@@ -1,5 +1,5 @@
 from src import *
-import os
+from concorde.tsp import TSPSolver
 
 
 def run(show_plots=False):
@@ -14,6 +14,15 @@ def run(show_plots=False):
         if show_plots:
             instance.plot_data()
 
+        solver = TSPSolver.from_data(
+            instance.points[:, 1]*100,
+            instance.points[:, 2]*100,
+            norm="EUC_2D"
+        )
+        solution = solver.solve(verbose=False)
+        tour_opt = np.copy(solution.tour)
+
+
         for method in ["random", "nearest_neighbors", "best_nn"]:
             solver = Solver_TSP(method)
             solver(instance, return_value=False)
@@ -24,6 +33,12 @@ def run(show_plots=False):
             if show_plots:
                 solver.plot_solution()
 
+        solver.method = "optimal"
+        solver.solution = np.concatenate([tour_opt, [tour_opt[0]]])
+        solver.solved = True
+        solver.plot_solution()
+        print(solver.evaluate_solution(return_value=True))
+
 
 if __name__ == '__main__':
     run(show_plots=True)

src/TSP_solver.py

@@ -64,7 +64,6 @@ class Solver_TSP:
         self.solved = True
         return self.solution
 
-
     def plot_solution(self):
         assert self.solved, "You can't plot the solution, you need to solve it first!"
         plt.figure(figsize=(8, 8))

src/io_tsp.py

@@ -51,7 +51,7 @@ class Instance:
     @staticmethod
     def distance_euc(zi, zj):
         xi, xj = zi[0], zj[0]
-        yi, yj = zi[0], zj[0]
+        yi, yj = zi[1], zj[1]
         return round(np.sqrt((xi - xj) ** 2 + (yi - yj) ** 2))
 
     def create_dist_matrix(self):