Refactored class_optimize.py

- Optimized Imports: Removed unused imports and organized them.
- Refactored Code: Introduced split_individual function for clarity.
- Improved Efficiency: Enhanced penalty calculation and streamlined loops.
- Updated Evaluation Logic: Better handling of penalties in evaluate.
- Type Hints added
- fixed seed option added for automated tests
- verbose comment added, default False

Notes:
- isfloat is only used in flask_server.py
- start_hour is not used in this class

modules/class_optimize.py

@@ -1,25 +1,23 @@
 import os
+import random
 import sys
+from typing import Any, Dict, List, Optional, Tuple
 
-import matplotlib
 import numpy as np
+from deap import algorithms, base, creator, tools
 
 from modules.class_akku import PVAkku
-from modules.class_ems import EnergieManagementSystem, Wechselrichter
+from modules.class_ems import EnergieManagementSystem
 from modules.class_haushaltsgeraet import Haushaltsgeraet
+from modules.class_inverter import Wechselrichter
 from modules.visualize import visualisiere_ergebnisse
 
-matplotlib.use("Agg")  # Setzt das Backend auf Agg
-import random
-from datetime import datetime, timedelta
-
-from deap import algorithms, base, creator, tools
-
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from config import moegliche_ladestroeme_in_prozent
 
 
-def isfloat(num):
+def isfloat(num: Any) -> bool:
+    """Check if a given input can be converted to float."""
     try:
         float(num)
         return True
@@ -27,281 +25,209 @@ def isfloat(num):
         return False
 
 
-def differential_evolution(
-    population,
-    toolbox,
-    cxpb,
-    mutpb,
-    ngen,
-    stats=None,
-    halloffame=None,
-    verbose=__debug__,
-):
-    """Differential Evolution Algorithm"""
-
-    # Evaluate the entire population
-    fitnesses = list(map(toolbox.evaluate, population))
-    for ind, fit in zip(population, fitnesses):
-        ind.fitness.values = fit
-
-    if halloffame is not None:
-        halloffame.update(population)
-
-    logbook = tools.Logbook()
-    logbook.header = ["gen", "nevals"] + (stats.fields if stats else [])
-
-    for gen in range(ngen):
-        # Generate the next generation by mutation and recombination
-        for i, target in enumerate(population):
-            a, b, c = random.sample([ind for ind in population if ind != target], 3)
-            mutant = toolbox.clone(a)
-            for k in range(len(mutant)):
-                mutant[k] = c[k] + mutpb * (a[k] - b[k])  # Mutation step
-                if random.random() < cxpb:  # Recombination step
-                    mutant[k] = target[k]
-
-            # Evaluate the mutant
-            mutant.fitness.values = toolbox.evaluate(mutant)
-
-            # Replace if mutant is better
-            if mutant.fitness > target.fitness:
-                population[i] = mutant
-
-        # Update hall of fame
-        if halloffame is not None:
-            halloffame.update(population)
-
-        # Gather all the fitnesses in one list and print the stats
-        record = stats.compile(population) if stats else {}
-        logbook.record(gen=gen, nevals=len(population), **record)
-        if verbose:
-            print(logbook.stream)
-
-    return population, logbook
-
-
 class optimization_problem:
-    def __init__(self, prediction_hours=24, strafe=10, optimization_hours=24):
-        self.prediction_hours = prediction_hours  #
+    def __init__(
+        self,
+        prediction_hours: int = 24,
+        strafe: float = 10,
+        optimization_hours: int = 24,
+        verbose: bool = False,
+        fixed_seed: Optional[int] = None,
+    ):
+        """Initialize the optimization problem with the required parameters."""
+        self.prediction_hours = prediction_hours
         self.strafe = strafe
         self.opti_param = None
         self.fixed_eauto_hours = prediction_hours - optimization_hours
         self.possible_charge_values = moegliche_ladestroeme_in_prozent
+        self.verbose = verbose
+        self.fix_seed = fixed_seed
 
-    def split_individual(self, individual):
+        # Set a fixed seed for random operations if provided
+        if fixed_seed is not None:
+            random.seed(fixed_seed)
+
+    def split_individual(
+        self, individual: List[float]
+    ) -> Tuple[List[int], List[float], Optional[int]]:
         """
-        Teilt das gegebene Individuum in die verschiedenen Parameter auf:
-        - Entladeparameter (discharge_hours_bin)
-        - Ladeparameter (eautocharge_hours_float)
-        - Haushaltsgeräte (spuelstart_int, falls vorhanden)
+        Split the individual solution into its components:
+        1. Discharge hours (binary),
+        2. Electric vehicle charge hours (float),
+        3. Dishwasher start time (integer if applicable).
         """
-        # Extrahiere die Entlade- und Ladeparameter direkt aus dem Individuum
-        discharge_hours_bin = individual[
-            : self.prediction_hours
-        ]  # Erste 24 Werte sind Bool (Entladen)
+        discharge_hours_bin = individual[: self.prediction_hours]
         eautocharge_hours_float = individual[
             self.prediction_hours : self.prediction_hours * 2
-        ]  # Nächste 24 Werte sind Float (Laden)
-
-        spuelstart_int = None
-        if self.opti_param and self.opti_param.get("haushaltsgeraete", 0) > 0:
-            spuelstart_int = individual[
-                -1
-            ]  # Letzter Wert ist Startzeit für Haushaltsgerät
-
+        ]
+        spuelstart_int = (
+            individual[-1]
+            if self.opti_param and self.opti_param.get("haushaltsgeraete", 0) > 0
+            else None
+        )
         return discharge_hours_bin, eautocharge_hours_float, spuelstart_int
 
-    def setup_deap_environment(self, opti_param, start_hour):
+    def setup_deap_environment(
+        self, opti_param: Dict[str, Any], start_hour: int
+    ) -> None:
+        """
+        Set up the DEAP environment with fitness and individual creation rules.
+        """
         self.opti_param = opti_param
 
-        if "FitnessMin" in creator.__dict__:
-            del creator.FitnessMin
-        if "Individual" in creator.__dict__:
-            del creator.Individual
+        # Remove existing FitnessMin and Individual classes from creator if present
+        for attr in ["FitnessMin", "Individual"]:
+            if attr in creator.__dict__:
+                del creator.__dict__[attr]
 
+        # Create new FitnessMin and Individual classes
         creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
         creator.create("Individual", list, fitness=creator.FitnessMin)
 
-        # PARAMETER
+        # Initialize toolbox with attributes and operations
         self.toolbox = base.Toolbox()
         self.toolbox.register("attr_bool", random.randint, 0, 1)
-        self.toolbox.register(
-            "attr_float", random.uniform, 0, 1
-        )  # Für kontinuierliche Werte zwischen 0 und 1 (z.B. für E-Auto-Ladeleistung)
-        # self.toolbox.register("attr_choice", random.choice, self.possible_charge_values)  # Für diskrete Ladeströme
-
+        self.toolbox.register("attr_float", random.uniform, 0, 1)
         self.toolbox.register("attr_int", random.randint, start_hour, 23)
 
-        ###################
-        # Haushaltsgeraete
-        # print("Haushalt:",opti_param["haushaltsgeraete"])
+        # Register individual creation method based on household appliance parameter
         if opti_param["haushaltsgeraete"] > 0:
-
-            def create_individual():
-                attrs = [
-                    self.toolbox.attr_bool() for _ in range(self.prediction_hours)
-                ]  # 24 Bool-Werte für Entladen
-                attrs += [
-                    self.toolbox.attr_float() for _ in range(self.prediction_hours)
-                ]  # 24 Float-Werte für Laden
-                attrs.append(self.toolbox.attr_int())  # Haushaltsgerät-Startzeit
-                return creator.Individual(attrs)
-
+            self.toolbox.register(
+                "individual",
+                lambda: creator.Individual(
+                    [self.toolbox.attr_bool() for _ in range(self.prediction_hours)]
+                    + [self.toolbox.attr_float() for _ in range(self.prediction_hours)]
+                    + [self.toolbox.attr_int()]
+                ),
+            )
         else:
+            self.toolbox.register(
+                "individual",
+                lambda: creator.Individual(
+                    [self.toolbox.attr_bool() for _ in range(self.prediction_hours)]
+                    + [self.toolbox.attr_float() for _ in range(self.prediction_hours)]
+                ),
+            )
 
-            def create_individual():
-                attrs = [
-                    self.toolbox.attr_bool() for _ in range(self.prediction_hours)
-                ]  # 24 Bool-Werte für Entladen
-                attrs += [
-                    self.toolbox.attr_float() for _ in range(self.prediction_hours)
-                ]  # 24 Float-Werte für Laden
-                return creator.Individual(attrs)
-
-        self.toolbox.register(
-            "individual", create_individual
-        )  # tools.initCycle, creator.Individual, (self.toolbox.attr_bool,self.toolbox.attr_bool), n=self.prediction_hours+1)
+        # Register population, mating, mutation, and selection functions
         self.toolbox.register(
             "population", tools.initRepeat, list, self.toolbox.individual
         )
         self.toolbox.register("mate", tools.cxTwoPoint)
         self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
-
-        # self.toolbox.register("mutate", mutate_choice, self.possible_charge_values, indpb=0.1)
-        # self.toolbox.register("mutate", tools.mutUniformInt, low=0, up=len(self.possible_charge_values)-1, indpb=0.1)
-
         self.toolbox.register("select", tools.selTournament, tournsize=3)
 
-    def evaluate_inner(self, individual, ems, start_hour):
+    def evaluate_inner(
+        self, individual: List[float], ems: EnergieManagementSystem, start_hour: int
+    ) -> Dict[str, Any]:
+        """
+        Internal evaluation function that simulates the energy management system (EMS)
+        using the provided individual solution.
+        """
         ems.reset()
-
-        # print("Spuel:",self.opti_param)
-
         discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
             self.split_individual(individual)
         )
-
-        # Haushaltsgeraete
-        if self.opti_param["haushaltsgeraete"] > 0:
+        if self.opti_param.get("haushaltsgeraete", 0) > 0:
             ems.set_haushaltsgeraet_start(spuelstart_int, global_start_hour=start_hour)
 
-        # discharge_hours_bin = np.full(self.prediction_hours,0)
         ems.set_akku_discharge_hours(discharge_hours_bin)
-
-        # Setze die festen Werte für die letzten x Stunden
-        for i in range(
-            self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours
-        ):
-            eautocharge_hours_float[i] = (
-                0.0  # Setze die letzten x Stunden auf einen festen Wert (oder vorgegebenen Wert)
-            )
-
-        # print(eautocharge_hours_float)
-
+        eautocharge_hours_float[self.prediction_hours - self.fixed_eauto_hours :] = [
+            0.0
+        ] * self.fixed_eauto_hours
         ems.set_eauto_charge_hours(eautocharge_hours_float)
+        return ems.simuliere(start_hour)
 
-        o = ems.simuliere(start_hour)
-
-        return o
-
-    # Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
-    def evaluate(self, individual, ems, parameter, start_hour, worst_case):
+    def evaluate(
+        self,
+        individual: List[float],
+        ems: EnergieManagementSystem,
+        parameter: Dict[str, Any],
+        start_hour: int,
+        worst_case: bool,
+    ) -> Tuple[float]:
+        """
+        Evaluate the fitness of an individual solution based on the simulation results.
+        """
         try:
             o = self.evaluate_inner(individual, ems, start_hour)
         except Exception:
-            return (100000.0,)
+            return (100000.0,)  # Return a high penalty in case of an exception
 
-        gesamtbilanz = o["Gesamtbilanz_Euro"]
-        if worst_case:
-            gesamtbilanz = gesamtbilanz * -1.0
-
-        discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
-            self.split_individual(individual)
+        gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
+        discharge_hours_bin, eautocharge_hours_float, _ = self.split_individual(
+            individual
         )
         max_ladeleistung = np.max(moegliche_ladestroeme_in_prozent)
 
-        strafe_überschreitung = 0.0
+        # Penalty for not discharging
+        gesamtbilanz += sum(
+            0.01 for i in range(self.prediction_hours) if discharge_hours_bin[i] == 0.0
+        )
 
-        # Ladeleistung überschritten?
-        for ladeleistung in eautocharge_hours_float:
-            if ladeleistung > max_ladeleistung:
-                # Berechne die Überschreitung
-                überschreitung = ladeleistung - max_ladeleistung
-                # Füge eine Strafe hinzu (z.B. 10 Einheiten Strafe pro Prozentpunkt Überschreitung)
-                strafe_überschreitung += (
-                    self.strafe * 10
-                )  # Hier ist die Strafe proportional zur Überschreitung
+        # Penalty for charging the electric vehicle during restricted hours
+        gesamtbilanz += sum(
+            self.strafe
+            for i in range(
+                self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours
+            )
+            if eautocharge_hours_float[i] != 0.0
+        )
 
-        # Für jeden Discharge 0, eine kleine Strafe von 1 Cent, da die Lastvertelung noch fehlt. Also wenn es egal ist, soll er den Akku entladen lassen
-        for i in range(0, self.prediction_hours):
-            if (
-                discharge_hours_bin[i] == 0.0
-            ):  # Wenn die letzten x Stunden von einem festen Wert abweichen
-                gesamtbilanz += 0.01  # Bestrafe den Optimierer
+        # Penalty for exceeding maximum charge power
+        gesamtbilanz += sum(
+            self.strafe * 10
+            for ladeleistung in eautocharge_hours_float
+            if ladeleistung > max_ladeleistung
+        )
 
-        # E-Auto nur die ersten self.fixed_eauto_hours
-        for i in range(
-            self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours
-        ):
-            if (
-                eautocharge_hours_float[i] != 0.0
-            ):  # Wenn die letzten x Stunden von einem festen Wert abweichen
-                gesamtbilanz += self.strafe  # Bestrafe den Optimierer
+        # Penalty for not meeting the minimum SOC (State of Charge) requirement
+        if parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent() <= 0.0:
+            gesamtbilanz += sum(
+                self.strafe
+                for ladeleistung in eautocharge_hours_float
+                if ladeleistung != 0.0
+            )
 
-        # Überprüfung, ob der Mindest-SoC erreicht wird
-        final_soc = (
-            ems.eauto.ladezustand_in_prozent()
-        )  # Nimmt den SoC am Ende des Optimierungszeitraums
-
-        if (parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent()) <= 0.0:
-            # print (parameter['eauto_min_soc']," " ,ems.eauto.ladezustand_in_prozent()," ",(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()))
-            for i in range(0, self.prediction_hours):
-                if (
-                    eautocharge_hours_float[i] != 0.0
-                ):  # Wenn die letzten x Stunden von einem festen Wert abweichen
-                    gesamtbilanz += self.strafe  # Bestrafe den Optimierer
-
-        eauto_roi = parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent()
         individual.extra_data = (
             o["Gesamtbilanz_Euro"],
             o["Gesamt_Verluste"],
-            eauto_roi,
+            parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent(),
         )
 
-        restenergie_akku = ems.akku.aktueller_energieinhalt()
-        restwert_akku = restenergie_akku * parameter["preis_euro_pro_wh_akku"]
-        # print(restenergie_akku)
-        # print(parameter["preis_euro_pro_wh_akku"])
-        # print(restwert_akku)
-        # print()
-        strafe = 0.0
-        strafe = max(
-            0,
-            (parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent())
-            * self.strafe,
+        # Adjust total balance with battery value and penalties for unmet SOC
+        restwert_akku = (
+            ems.akku.aktueller_energieinhalt() * parameter["preis_euro_pro_wh_akku"]
+        )
+        gesamtbilanz += (
+            max(
+                0,
+                (parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent())
+                * self.strafe,
+            )
+            - restwert_akku
         )
-        gesamtbilanz += strafe - restwert_akku + strafe_überschreitung
-        # gesamtbilanz += o["Gesamt_Verluste"]/10000.0
 
         return (gesamtbilanz,)
 
-    # Genetischer Algorithmus
-    def optimize(self, start_solution=None):
+    def optimize(
+        self, start_solution: Optional[List[float]] = None
+    ) -> Tuple[Any, Dict[str, List[Any]]]:
+        """Run the optimization process using a genetic algorithm."""
         population = self.toolbox.population(n=300)
         hof = tools.HallOfFame(1)
-
         stats = tools.Statistics(lambda ind: ind.fitness.values)
-        stats.register("avg", np.mean)
         stats.register("min", np.min)
-        stats.register("max", np.max)
 
-        print("Start:", start_solution)
+        if self.verbose:
+            print("Start optimize:", start_solution)
 
-        if start_solution is not None and start_solution != -1:
-            population.insert(0, creator.Individual(start_solution))
-            population.insert(1, creator.Individual(start_solution))
-            population.insert(2, creator.Individual(start_solution))
+        # Insert the start solution into the population if provided
+        if start_solution not in [None, -1]:
+            for _ in range(3):
+                population.insert(0, creator.Individual(start_solution))
 
+        # Run the evolutionary algorithm
         algorithms.eaMuPlusLambda(
             population,
             self.toolbox,
@@ -312,11 +238,8 @@ class optimization_problem:
             ngen=400,
             stats=stats,
             halloffame=hof,
-            verbose=True,
+            verbose=self.verbose,
         )
-        # algorithms.eaSimple(population, self.toolbox, cxpb=0.3, mutpb=0.3, ngen=200,             stats=stats, halloffame=hof, verbose=True)
-        # algorithms.eaMuCommaLambda(population, self.toolbox, mu=100, lambda_=200, cxpb=0.2, mutpb=0.4, ngen=300, stats=stats, halloffame=hof, verbose=True)
-        # population, log = differential_evolution(population, self.toolbox, cxpb=0.2, mutpb=0.5, ngen=200, stats=stats, halloffame=hof, verbose=True)
 
         member = {"bilanz": [], "verluste": [], "nebenbedingung": []}
         for ind in population:
@@ -329,42 +252,28 @@ class optimization_problem:
         return hof[0], member
 
     def optimierung_ems(
-        self, parameter=None, start_hour=None, worst_case=False, startdate=None
-    ):
-        ############
-        # Parameter
-        ############
-        if startdate is None:
-            date = (
-                datetime.now().date() + timedelta(hours=self.prediction_hours)
-            ).strftime("%Y-%m-%d")
-            date_now = datetime.now().strftime("%Y-%m-%d")
-        else:
-            date = (startdate + timedelta(hours=self.prediction_hours)).strftime(
-                "%Y-%m-%d"
-            )
-            date_now = startdate.strftime("%Y-%m-%d")
-        # print("Start_date:",date_now)
-
-        akku_size = parameter["pv_akku_cap"]  # Wh
-
+        self,
+        parameter: Optional[Dict[str, Any]] = None,
+        start_hour: Optional[int] = None,
+        worst_case: bool = False,
+        startdate: Optional[Any] = None,  # startdate is not used!
+    ) -> Dict[str, Any]:
+        """
+        Perform EMS (Energy Management System) optimization and visualize results.
+        """
         einspeiseverguetung_euro_pro_wh = np.full(
             self.prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]
-        )  # =  # € / Wh 7/(1000.0*100.0)
-        discharge_array = np.full(
-            self.prediction_hours, 1
-        )  # np.array([1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0])   #
+        )
+
+        # Initialize PV and EV batteries
         akku = PVAkku(
-            kapazitaet_wh=akku_size,
+            kapazitaet_wh=parameter["pv_akku_cap"],
             hours=self.prediction_hours,
             start_soc_prozent=parameter["pv_soc"],
             max_ladeleistung_w=5000,
         )
-        akku.set_charge_per_hour(discharge_array)
+        akku.set_charge_per_hour(np.full(self.prediction_hours, 1))
 
-        laden_moeglich = np.full(
-            self.prediction_hours, 1
-        )  # np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0])
         eauto = PVAkku(
             kapazitaet_wh=parameter["eauto_cap"],
             hours=self.prediction_hours,
@@ -373,121 +282,71 @@ class optimization_problem:
             max_ladeleistung_w=parameter["eauto_charge_power"],
             start_soc_prozent=parameter["eauto_soc"],
         )
-        eauto.set_charge_per_hour(laden_moeglich)
-        min_soc_eauto = parameter["eauto_min_soc"]
-        start_params = parameter["start_solution"]
+        eauto.set_charge_per_hour(np.full(self.prediction_hours, 1))
 
-        ###############
-        # spuelmaschine
-        ##############
-        print(parameter)
-        if parameter["haushaltsgeraet_dauer"] > 0:
-            spuelmaschine = Haushaltsgeraet(
+        # Initialize household appliance if applicable
+        spuelmaschine = (
+            Haushaltsgeraet(
                 hours=self.prediction_hours,
                 verbrauch_kwh=parameter["haushaltsgeraet_wh"],
                 dauer_h=parameter["haushaltsgeraet_dauer"],
-            )
-            spuelmaschine.set_startzeitpunkt(start_hour)  # Startet jetzt
-        else:
-            spuelmaschine = None
-
-        ###############
-        # PV Forecast
-        ###############
-        # PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
-        # PVforecast = PVForecast(prediction_hours = self.prediction_hours, url=pv_forecast_url)
-        # #print("PVPOWER",parameter['pvpowernow'])
-        # if isfloat(parameter['pvpowernow']):
-        # PVforecast.update_ac_power_measurement(date_time=datetime.now(), ac_power_measurement=float(parameter['pvpowernow']))
-        # #PVforecast.print_ac_power_and_measurement()
-        pv_forecast = parameter[
-            "pv_forecast"
-        ]  # PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
-        temperature_forecast = parameter[
-            "temperature_forecast"
-        ]  # PVforecast.get_temperature_for_date_range(date_now,date)
-
-        ###############
-        # Strompreise
-        ###############
-        specific_date_prices = parameter["strompreis_euro_pro_wh"]
-        print(specific_date_prices)
-        # print("https://api.akkudoktor.net/prices?start="+date_now+"&end="+date)
+            ).set_startzeitpunkt(start_hour)
+            if parameter["haushaltsgeraet_dauer"] > 0
+            else None
+        )
 
+        # Initialize the inverter and energy management system
         wr = Wechselrichter(10000, akku)
-
         ems = EnergieManagementSystem(
             gesamtlast=parameter["gesamtlast"],
-            pv_prognose_wh=pv_forecast,
-            strompreis_euro_pro_wh=specific_date_prices,
+            pv_prognose_wh=parameter["pv_forecast"],
+            strompreis_euro_pro_wh=parameter["strompreis_euro_pro_wh"],
             einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh,
             eauto=eauto,
             haushaltsgeraet=spuelmaschine,
             wechselrichter=wr,
         )
-        o = ems.simuliere(start_hour)
 
-        ###############
-        # Optimizer Init
-        ##############
-        opti_param = {}
-        opti_param["haushaltsgeraete"] = 0
-        if spuelmaschine is not None:
-            opti_param["haushaltsgeraete"] = 1
+        # Setup the DEAP environment and optimization process
+        self.setup_deap_environment(
+            {"haushaltsgeraete": 1 if spuelmaschine else 0}, start_hour
+        )
+        self.toolbox.register(
+            "evaluate",
+            lambda ind: self.evaluate(ind, ems, parameter, start_hour, worst_case),
+        )
+        start_solution, extra_data = self.optimize(parameter["start_solution"])
 
-        self.setup_deap_environment(opti_param, start_hour)
-
-        def evaluate_wrapper(individual):
-            return self.evaluate(individual, ems, parameter, start_hour, worst_case)
-
-        self.toolbox.register("evaluate", evaluate_wrapper)
-        start_solution, extra_data = self.optimize(start_params)
-        best_solution = start_solution
-        o = self.evaluate_inner(best_solution, ems, start_hour)
-        eauto = ems.eauto.to_dict()
-        spuelstart_int = None
+        # Perform final evaluation on the best solution
+        o = self.evaluate_inner(start_solution, ems, start_hour)
         discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
-            self.split_individual(best_solution)
+            self.split_individual(start_solution)
         )
 
-        print(parameter)
-        print(best_solution)
+        # Visualize the results
         visualisiere_ergebnisse(
             parameter["gesamtlast"],
-            pv_forecast,
-            specific_date_prices,
+            parameter["pv_forecast"],
+            parameter["strompreis_euro_pro_wh"],
             o,
             discharge_hours_bin,
             eautocharge_hours_float,
-            temperature_forecast,
+            parameter["temperature_forecast"],
             start_hour,
             self.prediction_hours,
             einspeiseverguetung_euro_pro_wh,
             extra_data=extra_data,
         )
+
         os.system("cp visualisierungsergebnisse.pdf ~/")
 
-        # 'Eigenverbrauch_Wh_pro_Stunde': eigenverbrauch_wh_pro_stunde,
-        # 'Netzeinspeisung_Wh_pro_Stunde': netzeinspeisung_wh_pro_stunde,
-        # 'Netzbezug_Wh_pro_Stunde': netzbezug_wh_pro_stunde,
-        # 'Kosten_Euro_pro_Stunde': kosten_euro_pro_stunde,
-        # 'akku_soc_pro_stunde': akku_soc_pro_stunde,
-        # 'Einnahmen_Euro_pro_Stunde': einnahmen_euro_pro_stunde,
-        # 'Gesamtbilanz_Euro': gesamtkosten_euro,
-        # 'E-Auto_SoC_pro_Stunde':eauto_soc_pro_stunde,
-        # 'Gesamteinnahmen_Euro': sum(einnahmen_euro_pro_stunde),
-        # 'Gesamtkosten_Euro': sum(kosten_euro_pro_stunde),
-        # "Verluste_Pro_Stunde":verluste_wh_pro_stunde,
-        # "Gesamt_Verluste":sum(verluste_wh_pro_stunde),
-        # "Haushaltsgeraet_wh_pro_stunde":haushaltsgeraet_wh_pro_stunde
-
-        # print(eauto)
+        # Return final results as a dictionary
         return {
             "discharge_hours_bin": discharge_hours_bin,
             "eautocharge_hours_float": eautocharge_hours_float,
             "result": o,
-            "eauto_obj": eauto,
-            "start_solution": best_solution,
+            "eauto_obj": ems.eauto.to_dict(),
+            "start_solution": start_solution,
             "spuelstart": spuelstart_int,
             "simulation_data": o,
         }