diff --git a/Appli-iso3dfd/ACO.py b/Appli-iso3dfd/ACO.py
index cd3f9eb72121ba51651a9d23cc486e9e91a1c7e8..204cdd63a37038a6361047d441b0861d13579c87 100644
--- a/Appli-iso3dfd/ACO.py
+++ b/Appli-iso3dfd/ACO.py
@@ -95,7 +95,7 @@ def ACO(Me, NbP, comm, alpha, rho, Q, nb_ants, tau_0, n_iter, n1_max=256, n2_max
                     best_p = paths[np.argmin(costs), :]
 
             if fancy_strategy == 'ElitistAS':
-                tau = tools.add_pheromones(tau, np.array(best_p), np.array(best_cost), Q, n1_size, n2_size, n3_size, n_cbx, n_cby)
+                tau = tools.add_pheromones(tau, np.array([best_p]), np.array([[best_cost]]), Q, n1_size, n2_size, n3_size, n_cbx, n_cby)
 
         if fancy_strategy == 'MMAS':
             #we compute the best path locally
@@ -110,7 +110,7 @@ def ACO(Me, NbP, comm, alpha, rho, Q, nb_ants, tau_0, n_iter, n1_max=256, n2_max
 
             #we compute the pheromon using only the best path
             tau = tau * (1-rho)
-            tau = tools.add_pheromones(tau, np.array(best_p), np.array(best_cost), Q, n1_size, n2_size, n3_size, n_cbx, n_cby)
+            tau = tools.add_pheromones(tau, np.array([best_p]), np.array([[best_cost]]), Q, n1_size, n2_size, n3_size, n_cbx, n_cby)
 
             #verification of the threshold constraint
             size = np.size(tau)