feat multiple goal expressions

src/orchestration_config.json

@@ -6,7 +6,7 @@
     "SIMULATION_TIME": 100,
     "TIME_CONFIG": {
         "START_TIME": 8,
-        "END_TIME": 13,
+        "END_TIME": 12,
         "TIME_UNIT": "hour"
     },
     "OBJECTIVES": [
@@ -31,7 +31,10 @@
         "user_demand": "userDemand"
     },
     "CRITERIA": {
-        "GOAL_EXPRESSION": "evaluation_results['performance'] >= simulation_inputs['user_demand']"
+        "GOAL_EXPRESSION": [
+            "evaluation_results['performance'] >= simulation_inputs['user_demand']",
+            "evaluation_results['remainingEnergy'] >= 0"
+        ]
     },
     "OPTIMIZATION_CONFIG": {
         "USE_SINGLE_OBJECTIVE": false,

src/orchestration_configurator.py

@@ -76,7 +76,8 @@ def simulate_and_evaluate(parameters, simulation_time, simulation_inputs, orches
             print(f"{criterion}: {result_value}")
 
         # Evaluate the goal expression dynamically
-        goal_satisfied = eval(goal_expression)
+        local_vars = {"evaluation_results": evaluation_results, "simulation_inputs": simulation_inputs}
+        goal_satisfied = all(eval(expression, {}, local_vars) for expression in goal_expression)
         print(f"Goal satisfied: {goal_satisfied}")
 
         return evaluation_results, goal_satisfied, parameters, depletion_time
@@ -107,16 +108,12 @@ def adaptive_control_loop(data, moo_wrapper, orchestration_config):
             
             # Create a dictionary of inputs
             simulation_inputs = dict(zip(input_keys, input_values))  
-            
             # Update the config for the optimization framework
             moo_wrapper.update_config(simulation_inputs, simulation_time)
-
             # Run the optimization using the wrapper
             moo_wrapper.run_optimization()
-            
             # Get the list of best parameters from the optimization results
             parameter_list = moo_wrapper.get_parameters()
-            
             # Try each parameter set in order until goal is satisfied or options are exhausted
             goal_satisfied = False