diff --git a/src/TSP_solver.py b/src/TSP_solver.py
index 7364c3b..1604113 100644
--- a/src/TSP_solver.py
+++ b/src/TSP_solver.py
@@ -62,7 +62,7 @@ class Solver_TSP:
             print(f"###  solution found with {self.gap} % gap  in {self.time_to_solve} seconds ####")
             print(f"the total length for the solution found is {self.found_length}",
                   f"while the optimal length is {self.instance.best_sol}",
-                  f"the gap is {self.solver.gap}%",
+                  f"the gap is {self.gap}%",
                   f"the solution is found in {self.time_to_solve} seconds", sep="\n")
 
         if return_value:
diff --git a/src/meta_heuristics.py b/src/meta_heuristics.py
index fa7677d..e86f5c1 100644
--- a/src/meta_heuristics.py
+++ b/src/meta_heuristics.py
@@ -13,9 +13,9 @@ class Simulated_Annealing:
 
         # initial setup
         temperature = instance.best_sol / np.sqrt(instance.nPoints)
-        current_sol = solution
+        current_sol = np.array(solution)
         current_len = compute_lenght(solution, instance.dist_matrix)
-        best_sol = solution
+        best_sol = np.array(solution)
         best_len = current_len
 
         # main loop
@@ -41,7 +41,8 @@ class Simulated_Annealing:
     def random_sol_from_neig(solution, instance):
         i, j = np.random.choice(np.arange(1, len(solution) - 1), 2, replace=False)
         i, j = np.sort([i, j])
-        return Simulated_Annealing.swap2opt(solution, i, j), Simulated_Annealing.gain()
+        return Simulated_Annealing.swap2opt(solution, i, j), Simulated_Annealing.gain(i, j, solution,
+                                                                                      instance.dist_matrix)
 
     @staticmethod
     def swap2opt(tsp_sequence, i, j):