diff --git a/backend/src/utils/optimizer.py b/backend/src/utils/optimizer.py
index becdc76..e5d4811 100644
--- a/backend/src/utils/optimizer.py
+++ b/backend/src/utils/optimizer.py
@@ -41,7 +41,7 @@ class Optimizer:
             resx (list[float]): List of edge weights.
 
         Returns:
-            tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
+            tuple[list[int], list[int]]: A tuple containing a new row for A and new value for ub.
         """
         
         for i, elem in enumerate(resx):
@@ -165,19 +165,21 @@ class Optimizer:
 
 
 
-    def init_ub_dist(self, landmarks: list[Landmark], max_time: int):
+    def init_ub_time(self, landmarks: list[Landmark], max_time: int):
         """
-        Initialize the objective function coefficients and inequality constraints for the optimization problem.
+        Initialize the objective function coefficients and inequality constraints.
 
-        This function computes the distances between all landmarks and stores their attractiveness to maximize sightseeing. 
-        The goal is to maximize the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
+        This function computes the distances between all landmarks and stores
+        their attractiveness to maximize sightseeing. The goal is to maximize 
+        the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
 
         Args:
             landmarks (list[Landmark]): List of landmarks.
             max_time (int): Maximum time of visit allowed.
 
         Returns:
-            tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
+            tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality
+            constraint coefficients, and the right-hand side of the inequality constraint.
         """
         
         # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
@@ -191,7 +193,7 @@ class Optimizer:
             for j, spot2 in enumerate(landmarks) :
                 t = get_time(spot1.location, spot2.location) + spot1.duration
                 dist_table[j] = t
-            closest = sorted(dist_table)[:25]
+            closest = sorted(dist_table)[:15]
             for i, dist in enumerate(dist_table) :
                 if dist not in closest :
                     dist_table[i] = 32700
@@ -452,7 +454,7 @@ class Optimizer:
         L = len(landmarks)
 
         # SET CONSTRAINTS FOR INEQUALITY
-        c, A_ub, b_ub = self.init_ub_dist(landmarks, max_time)          # Add the distances from each landmark to the other
+        c, A_ub, b_ub = self.init_ub_time(landmarks, max_time)          # Add the distances from each landmark to the other
         A, b = self.respect_number(L, max_landmarks)                                   # Respect max number of visits (no more possible stops than landmarks). 
         A_ub = np.vstack((A_ub, A), dtype=np.int16)
         b_ub += b