fix(dra): stabilize Pareto front with neighborhood spreading and dampened utility

Author: MichalRedm

src/portfolio/moead_dra.py

@@ -65,9 +65,7 @@ def generate_pareto_front(
             else:
                 # 30% of population is updated in focused mode
                 size = max(1, int(0.3 * num_points))
-                selection = self._tournament_selection(
-                    utility, size, weight_vectors
-                )
+                selection = self._tournament_selection(utility, size, weight_vectors)
 
             for i in selection:
                 nb_idx = neighbors[i]
@@ -90,12 +88,14 @@ def generate_pareto_front(
                 off_fn = self._normalise(off_f)
 
                 # Update neighbors using shared logic
+                # For DRA, we use nr = T (full neighborhood) to spread progress
+                # and smooth out the front, since fewer parents are updated.
                 self._update_neighborhood(
                     offspring,
                     off_f,
                     off_fn,
                     nb_idx,
-                    nr,
+                    T,
                     weight_vectors,
                     population,
                     f_phys,
@@ -124,10 +124,14 @@ def generate_pareto_front(
 
                 for k in range(num_points):
                     if delta[k] > 0.0001:
-                        utility[k] = 1.0
+                        # Dampened success: Blend new utility (1.0) with old
+                        utility[k] = 0.9 * utility[k] + 0.1 * 1.0
                     else:
-                        # Slightly slower decay (0.98) and lower threshold
-                        utility[k] = (0.98 + 0.02 * delta[k] / 0.0001) * utility[k]
+                        # Slightly slower decay (0.99) for stability
+                        utility[k] = (0.99 + 0.01 * delta[k] / 0.0001) * utility[k]
+
+                    # Ensure a minimum utility so no subproblem is permanently ignored
+                    utility[k] = max(utility[k], 0.01)
 
                 old_g = new_g
 
@@ -145,15 +149,15 @@ def generate_pareto_front(
     def _tournament_selection(self, utility, size, weight_vectors):
         num_points = len(utility)
         selected = []
-        
+
         # 1. 90% Selection based on utility (Tournament)
         # Reduced tournament size (5) for better exploration
         n_tournament = int(0.9 * size)
         for _ in range(n_tournament):
             candidates = np.random.choice(num_points, 5, replace=False)
             best = candidates[np.argmax(utility[candidates])]
             selected.append(best)
-            
+
         # 2. 10% Pure Random Selection to prevent local optima
         n_random = size - n_tournament
         if n_random > 0: