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fix: use per-dimension random vectors r1/r2 in PSO velocity update (#87) #100

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fix: use per-dimension random vectors r1/r2 in PSO velocity update (#87) #100

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6b0675b
fix: ensure minimum sigma of 1.0 in Hill Climbing discrete iteration
mohammed18salah Jul 12, 2026
53f1658
fix: use per-dimension random vectors r1/r2 in PSO velocity update
mohammed18salah Jul 12, 2026
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4 changes: 2 additions & 2 deletions src/gradient_free_optimizers/optimizers/local_opt/hill_climbing_optimizer.py
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Expand Up @@ -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]
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14 changes: 10 additions & 4 deletions src/gradient_free_optimizers/optimizers/pop_opt/particle_swarm_optimization.py
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Expand Up @@ -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
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