diff --git a/pyoptsparse/pyALPSO/pyALPSO.py b/pyoptsparse/pyALPSO/pyALPSO.py
index 2362a6ad..6268618c 100644
--- a/pyoptsparse/pyALPSO/pyALPSO.py
+++ b/pyoptsparse/pyALPSO/pyALPSO.py
@@ -192,9 +192,7 @@ def objconfunc(x):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
-            sol_inform = {}
-            sol_inform["value"] = None
-            sol_inform["text"] = None
+            sol_inform = {"value": "", "text": ""}
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, opt_f, opt_x)
diff --git a/pyoptsparse/pyCONMIN/pyCONMIN.py b/pyoptsparse/pyCONMIN/pyCONMIN.py
index 7d010a50..9261a0a6 100644
--- a/pyoptsparse/pyCONMIN/pyCONMIN.py
+++ b/pyoptsparse/pyCONMIN/pyCONMIN.py
@@ -241,9 +241,7 @@ def cnmngrad(n1, n2, x, f, g, ct, df, a, ic, nac):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
-            sol_inform = {}
-            sol_inform["value"] = None
-            sol_inform["text"] = None
+            sol_inform = {"value": "", "text": ""}
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, ff, xs)
diff --git a/pyoptsparse/pyNSGA2/pyNSGA2.py b/pyoptsparse/pyNSGA2/pyNSGA2.py
index 9ed871ec..6b0cc302 100644
--- a/pyoptsparse/pyNSGA2/pyNSGA2.py
+++ b/pyoptsparse/pyNSGA2/pyNSGA2.py
@@ -181,9 +181,7 @@ def objconfunc(nreal, nobj, ncon, x, f, g):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
-            sol_inform = {}
-            sol_inform["value"] = None
-            sol_inform["text"] = None
+            sol_inform = {"value": "", "text": ""}
 
             xstar = [0.0] * n
             for i in range(n):
diff --git a/pyoptsparse/pyParOpt/ParOpt.py b/pyoptsparse/pyParOpt/ParOpt.py
index f08e33b4..e0e15be4 100644
--- a/pyoptsparse/pyParOpt/ParOpt.py
+++ b/pyoptsparse/pyParOpt/ParOpt.py
@@ -235,9 +235,7 @@ def evalObjConGradient(self, x, g, A):
             # Create the optimization solution. Note that the signs on the multipliers
             # are switch since ParOpt uses a formulation with c(x) >= 0, while pyOpt
             # uses g(x) = -c(x) <= 0. Therefore the multipliers are reversed.
-            sol_inform = {}
-            sol_inform["value"] = None
-            sol_inform["text"] = None
+            sol_inform = {"value": "", "text": ""}
 
             # If number of constraints is zero, ParOpt returns z as None.
             # Thus if there is no constraints, should pass an empty list