diff --git a/AgRobotSwarmForOptimization/field_optimization.ipynb b/AgRobotSwarmForOptimization/field_optimization.ipynb
index 64d00ab1e679f45d66ad7c033299d405661ac4f7..aed48d2b09d91600b3b4319fd83d27e0ae00ed2b 100644
--- a/AgRobotSwarmForOptimization/field_optimization.ipynb
+++ b/AgRobotSwarmForOptimization/field_optimization.ipynb
@@ -17,12 +17,14 @@
     "    terminate = True\n",
     "    return current_state, terminate\n",
     "\n",
-    "def t_schedule(t: int) -> float:\n",
-    "    t_max = 100\n",
-    "    return t_max - t\n",
     "\n",
     "MAX_ITERATIONS = 50\n",
     "\n",
+    "def t_schedule(t: int) -> float:\n",
+    "    return (MAX_ITERATIONS - t) / MAX_ITERATIONS\n",
+    "\n",
+    "\n",
+    "\n",
     "optimize_sim = Simulation(field_save_dir=\"field\", t_schedule=t_schedule, stochastic_hc=stochastic_hc_successor, random_search=random_search_successor)\n",
     "optimize_sim.prepare_complete_state_representation()\n",
     "field_vis = FieldVisualizer(optimize_sim, canvas_size=600)\n",
diff --git a/AgRobotSwarmForOptimization/field_optimization.py b/AgRobotSwarmForOptimization/field_optimization.py
index 1c91e2b3aa04f9b6a9421ba9d529380be98b5971..9ba85e4e35cc70909720772fbf41a4091531a106 100644
--- a/AgRobotSwarmForOptimization/field_optimization.py
+++ b/AgRobotSwarmForOptimization/field_optimization.py
@@ -314,7 +314,7 @@ class Simulation:
         # get t
         t = len(self.cost_history)
         # calculate temperature with function of user
-        temperature = self.t_schedule(t)
+        temperature = self.t_schedule(t) * 100
         if temperature <= 0:
             return self.state, True
         # choose random successor
@@ -327,10 +327,14 @@ class Simulation:
         cost = self.evaluate_state(state=neighbor_candidate_state)
 
         delta_e = cost - self.cost_history[-1]
+
         if delta_e < 0:
             return neighbor_candidate_state, False
         else:
-            propability = np.exp(delta_e / temperature)
+            propability = np.exp(-delta_e / temperature)
+            print(
+                f"Keine Verbesserung mit delta_e {delta_e:.2f}, temperature {temperature / 100:.2f} und Annahmewahrscheinlichkeit {propability * 100:.2f}%"
+            )
             if random.random() <= propability:
                 return neighbor_candidate_state, False
             else:
diff --git a/AgRobotSwarmForOptimization/graph_optimization.ipynb b/AgRobotSwarmForOptimization/graph_optimization.ipynb
index a051188219b590474601f6cc8b1862394681cf86..c9ff1c90f9d01e6342bc6d887c6011780945349a 100644
--- a/AgRobotSwarmForOptimization/graph_optimization.ipynb
+++ b/AgRobotSwarmForOptimization/graph_optimization.ipynb
@@ -21,11 +21,10 @@
     "\n",
     "\n",
     "###### In diesem Teil könnt ihr Änderungen vornehmen ######\n",
+    "MAX_ITERATIONS = 50\n",
     "def t_schedule(t: int) -> float:\n",
-    "    t_max = 100\n",
-    "    return t_max - t\n",
+    "    return (MAX_ITERATIONS - t) / MAX_ITERATIONS\n",
     "\n",
-    "MAX_ITERATIONS = 50\n",
     "MANUAL_STATE_TEST = [\"Hof\", 0, 1, 2, 3, \"Hof\", 4, 5, 6, 7]\n",
     "###### Teil-Ende ######\n",
     "\n",
diff --git a/AgRobotSwarmForOptimization/graph_optimization.py b/AgRobotSwarmForOptimization/graph_optimization.py
index dfd59aef1c2aafbc8bf55c03bb7a434d9dc3de48..9a9029fdd8a998f4bff5d3ba428acb13280653e7 100644
--- a/AgRobotSwarmForOptimization/graph_optimization.py
+++ b/AgRobotSwarmForOptimization/graph_optimization.py
@@ -229,7 +229,7 @@ class Simulation:
         # get t
         t = len(self.cost_history)
         # calculate temperature with function of user
-        temperature = self.t_schedule(t)
+        temperature = self.t_schedule(t) * 100
         if temperature <= 0:
             return self.state, True
         # choose random successor
@@ -245,7 +245,10 @@ class Simulation:
         if delta_e < 0:
             return neighbor_candidate_state, False
         else:
-            propability = np.exp(delta_e / temperature)
+            propability = np.exp(-delta_e / temperature)
+            print(
+                f"Keine Verbesserung mit delta_e {delta_e:.2f}, temperature {temperature / 100:.2f} und Annahmewahrscheinlichkeit {propability * 100:.2f}%"
+            )
             if random.random() <= propability:
                 return neighbor_candidate_state, False
             else: