fix MMAS

Appli-iso3dfd/ACO.py

@@ -103,8 +103,9 @@ 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), :]
 
             #we compare the best paths of all process and we save only the best one
-            recv_table = np.empty(2)
-            comm.Allreduce(np.array([best_cost, Me]), recv_table, OP=MPI.MINLOC)
+            recv_table = (0., 0)
+            send_table = (best_cost, Me)
+            recv_table = comm.allreduce(send_table, op=MPI.MINLOC)
             best_cost = recv_table[0]
             best_p = comm.bcast(best_p, root=recv_table[1])
 
@@ -113,13 +114,7 @@ def ACO(Me, NbP, comm, alpha, rho, Q, nb_ants, tau_0, n_iter, n1_max=256, n2_max
             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)
-            for i in range(size[0]):
-                for j in range(size[1]):
-                    if tau[i][j] < sub_threshold:
-                        tau[i][j] = sub_threshold
-                    if tau[i][j] > sup_threshold:
-                        tau[i][j] = sup_threshold
+            np.clip(tau, sub_threshold, sup_threshold)
 
         #we update the best path on all iteration
         if best_cost < cost_opti: