diff --git a/7.24.py b/7.24.py
new file mode 100644
index 0000000..b280f60
--- /dev/null
+++ b/7.24.py
@@ -0,0 +1,147 @@
+import numpy as np
+import pandas as pd
+import time
+import os
+import json
+from concurrent.futures import ThreadPoolExecutor
+from deap import base, creator, tools, algorithms
+
+
+def fitness_numpy(individual, price, load, temperature, irradiance, wind_speed, prev_soc, prev_Pg1, prev_Pg2, prev_Pg3):
+ individual = np.array(individual)
+ price = np.array(price)
+ load = np.array(load)
+ temperature = np.array(temperature)
+ irradiance = np.array(irradiance)
+ wind_speed = np.array(wind_speed)
+
+ Ac, Ag1, Ag2, Ag3, Av = individual[:5]
+ soc = np.clip(prev_soc + 0.2 * Ac * 0.9, 0.2, 0.8)
+ Pg1 = np.clip(prev_Pg1 + 100 * Ag1, 0, 150)
+ Pg2 = np.clip(prev_Pg2 + 100 * Ag2, 0, 375)
+ Pg3 = np.clip(prev_Pg3 + 200 * Ag3, 0, 500)
+ Pso = np.clip((0.2 * irradiance + 0.05 * temperature - 9.25) * (1 + Av), 0, None)
+ Pw = np.where((wind_speed >= 3) & (wind_speed < 8), wind_speed ** 3 * 172.2625 / 1000,
+ np.where((wind_speed >= 8) & (wind_speed < 12), 64 * 172.2625 / 125, 0))
+ P = Ac + Pg1 + Pg2 + Pg3 + Pso + Pw
+
+ Ee = np.where(P >= load, P - load, 0)
+ Es = np.where(P < load, load - P, 0)
+
+ Cb = 0.01 * Ac + 0.1 * soc
+ Cg1 = 0.0034 * Pg1 ** 2 + 3 * Pg1 + 30
+ Cg2 = 0.001 * Pg2 ** 2 + 10 * Pg2 + 40
+ Cg3 = 0.001 * Pg3 ** 2 + 15 * Pg3 + 70
+ Cs = 0.01 * Pso
+ Cw = 0.01 * Pw
+ Rs = 0.5 * price * Ee
+ Cp = price * Es
+ Pe = np.where(Ee > 100, (Ee - 100) * 50, 0)
+ Ps = np.where(Es > 100, (Es - 100) * 50, 0)
+
+ total_cost = np.sum(Cb + Cg1 + Cg2 + Cg3 + Cs + Cw - Rs + Cp + Pe + Ps)
+ reward = -total_cost / 1000
+
+ return (reward,)
+
+
+def check_bounds(func):
+ def wrapper(*args, **kwargs):
+ offspring = func(*args, **kwargs)
+ for individual in offspring:
+ for i in range(len(individual)):
+ individual[i] = np.clip(individual[i], -1, 1)
+ return offspring
+ return wrapper
+
+
+def main():
+ data = pd.read_csv('./data.csv')
+
+ price = data['price'].values
+ load = data['load'].values
+ temperature = data['temperature'].values
+ irradiance = data['irradiance'].values
+ wind_speed = data['wind_speed'].values
+
+ creator.create("FitnessMax", base.Fitness, weights=(1.0,))
+ creator.create("Individual", list, fitness=creator.FitnessMax)
+
+ toolbox = base.Toolbox()
+ toolbox.register("attr_float", np.random.uniform, -1, 1)
+ toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=5)
+ toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+
+ toolbox.register("mate", check_bounds(tools.cxBlend), alpha=0.5)
+ toolbox.register("mutate", check_bounds(tools.mutGaussian), mu=0, sigma=0.1, indpb=0.2)
+ toolbox.register("select", tools.selTournament, tournsize=3)
+ toolbox.register("evaluate", fitness_numpy)
+
+ population = toolbox.population(n=500)
+ prev_soc, prev_Pg1, prev_Pg2, prev_Pg3 = 0.4, 0.0, 0.0, 0.0
+ decision_values = []
+
+ for hour in range(8760):
+ start = time.time()
+ current_price = price[hour]
+ current_load = load[hour]
+ current_temperature = temperature[hour]
+ current_irradiance = irradiance[hour]
+ current_wind_speed = wind_speed[hour]
+
+ for gen in range(500):
+ offspring = toolbox.select(population, len(population))
+ offspring = list(map(toolbox.clone, offspring))
+
+ for child1, child2 in zip(offspring[::2], offspring[1::2]):
+ if np.random.rand() < 0.7:
+ toolbox.mate(child1, child2)
+ del child1.fitness.values
+ del child2.fitness.values
+ for mutant in offspring:
+ if np.random.rand() < 0.2:
+ toolbox.mutate(mutant)
+ del mutant.fitness.values
+
+ invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
+
+ with ThreadPoolExecutor() as executor:
+ futures = []
+ batch_size = 500
+
+ for i in range(0, len(invalid_ind), batch_size):
+ batch = invalid_ind[i:i + batch_size]
+ for ind in batch:
+ futures.append(
+ executor.submit(toolbox.evaluate, ind, current_price, current_load, current_temperature,
+ current_irradiance, current_wind_speed, prev_soc, prev_Pg1, prev_Pg2,
+ prev_Pg3))
+
+ for future in futures:
+ fitness = future.result()
+ for ind in invalid_ind:
+ if not ind.fitness.valid:
+ ind.fitness.values = fitness
+
+ population[:] = offspring
+ end = time.time()
+ best_ind = tools.selBest(population, 1)[0]
+ print(f'Best individual at hour {hour + 1}: {best_ind}')
+ print(f'Fitness: {best_ind.fitness.values}, using {end - start}s')
+
+ decision_values.append({
+ 'Ac': best_ind[0],
+ 'Ag1': best_ind[1],
+ 'Ag2': best_ind[2],
+ 'Ag3': best_ind[3],
+ 'Av': best_ind[4]
+ })
+
+ prev_soc, prev_Pg1, prev_Pg2, prev_Pg3 = best_ind[0], best_ind[1], best_ind[2], best_ind[3]
+
+ with open('decision_values.json', 'w') as f:
+ json.dump(decision_values, f)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/AgentSAC/DRL__plots/Evoluation Information.svg b/AgentSAC/DRL__plots/Evoluation Information.svg
deleted file mode 100644
index fdad3f8..0000000
--- a/AgentSAC/DRL__plots/Evoluation Information.svg
+++ /dev/null
@@ -1,4508 +0,0 @@
-
-
-
diff --git a/AgentSAC/DRL__plots/optimization_information.svg b/AgentSAC/DRL__plots/optimization_information.svg
index 579bb84..dc215e5 100644
--- a/AgentSAC/DRL__plots/optimization_information.svg
+++ b/AgentSAC/DRL__plots/optimization_information.svg
@@ -1,12 +1,12 @@
-