diff --git a/src/gradient_free_optimizers/optimizers/local_opt/hill_climbing_optimizer.py b/src/gradient_free_optimizers/optimizers/local_opt/hill_climbing_optimizer.py
index f1e1354d..879c3a3f 100644
--- a/src/gradient_free_optimizers/optimizers/local_opt/hill_climbing_optimizer.py
+++ b/src/gradient_free_optimizers/optimizers/local_opt/hill_climbing_optimizer.py
@@ -191,8 +191,8 @@ def _iterate_discrete_batch(self) -> ndarray:
         max_positions = bounds[:, 1]
         sigmas = max_positions * self.epsilon
 
-        # Prevent zero sigma for single-value dimensions
-        sigmas = maximum(sigmas, 1e-10)
+        # Prevent getting stuck: ensure noise standard deviation is at least 1.0 index
+        sigmas = maximum(sigmas, 1.0)
 
         # Generate noise using the configured distribution
         noise_fn = self._DISTRIBUTIONS[self.distribution]
diff --git a/src/gradient_free_optimizers/optimizers/pop_opt/particle_swarm_optimization.py b/src/gradient_free_optimizers/optimizers/pop_opt/particle_swarm_optimization.py
index a1b6c9a1..5eceb14b 100644
--- a/src/gradient_free_optimizers/optimizers/pop_opt/particle_swarm_optimization.py
+++ b/src/gradient_free_optimizers/optimizers/pop_opt/particle_swarm_optimization.py
@@ -219,19 +219,25 @@ def _compute_pso_position(self) -> ndarray:
         ):
             return self.p_current.init.move_random_typed()
 
-        r1, r2 = random.random(), random.random()
-
         pos_current = array(self.p_current._pos_current)
         pos_best = array(self.p_current._pos_best)
         global_pos_best = array(self.p_current.global_pos_best)
 
+        # Per-dimension random coefficients (Kennedy & Eberhart, 1995).
+        # Using scalar r1/r2 couples all dimensions to the same random factor,
+        # creating correlated diagonal movement and preventing independent
+        # per-dimension exploration. Vectors ensure stochastic independence.
+        n_dims = len(pos_current)
+        r1 = array([random.random() for _ in range(n_dims)])
+        r2 = array([random.random() for _ in range(n_dims)])
+
         # Inertia term: maintain current direction
         A = self.inertia * array(self.p_current.velo)
 
-        # Cognitive term: attract toward personal best
+        # Cognitive term: attract toward personal best (per-dimension)
         B = self.cognitive_weight * r1 * (pos_best - pos_current)
 
-        # Social term: attract toward global best
+        # Social term: attract toward global best (per-dimension)
         C = self.social_weight * r2 * (global_pos_best - pos_current)
 
         # Update velocity