KuMiShi/Optim_Metaheuristique
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updating main.py

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Amimine
2026-01-18 17:29:46 +01:00
parent ac5cbbc690
commit ca254e97ac
1 changed files with 165 additions and 27 deletions
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main.py
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@@ -1,12 +1,45 @@
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import copy
from mopso import MOPSO
from surrogate_handler import SurrogateHandler
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # Nécessaire pour la 3D
def plot_pareto_3d(archive, model_type:str):
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
# Extraction des scores depuis l'archive
# f_best[0] = Coût, f_best[1] = Insatisfaction, f_best[2] = Stress Réseau
f1 = [p.f_best[0] for p in archive]
f2 = [p.f_best[1] for p in archive]
f3 = [p.f_best[2] for p in archive]
# Création du nuage de points
img = ax.scatter(f1, f2, f3, c=f3, cmap='viridis', s=60, edgecolors='black')
ax.set_xlabel('Coût (€)')
ax.set_ylabel('Insatisfaction (SoC manquant)')
ax.set_zlabel('Pic Réseau (kW)')
ax.set_title(f'Front de Pareto des Solutions Non-Dominées ({model_type})')
# Barre de couleur
cbar = fig.colorbar(img, ax=ax, pad=0.1)
cbar.set_label('Intensité du Pic Réseau (kW)')
# Sauvegarde et affichage
filename = f"{model_type}_pareto_3d.png"
plt.savefig(filename)
print(f"Graphique sauvegardé sous : {filename}")
plt.show()
# --- EXTENDED CLASS (Inheritance) ---
class SmartMOPSO(MOPSO):
def __init__(self, model_type=None, **kwargs):
super().__init__(**kwargs)
@@ -43,18 +76,18 @@ class SmartMOPSO(MOPSO):
self.particles[i].keep_boudaries(self.A_max)
if use_ai:
# 1. Fast exact calculation (f1, f3)
# Fast exact calculation (f1, f3)
f1 = self.particles[i].f1(self.prices)
f3 = self.particles[i].f3()
# 2. Slow prediction (f2) via AI
# Slow prediction (f2) by using Surrogate
f2_pred = self.surrogate_handler.predict(self.particles[i].x)
# 3. Inject scores without running the expensive 'updating_socs'
# Inject scores without running the expensive 'updating_socs'
self.particles[i].f_current = [f1, f2_pred, f3]
else:
# Standard Calculation (Slow & Exact)
# Standard Calculation (Slow and Exact)
self.particles[i].updating_socs(self.socs, self.capacities)
self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times)
@@ -66,8 +99,9 @@ class SmartMOPSO(MOPSO):
self.update_archive()
def calculate_elec_prices(csv_file:str, sep:str=';'):
elec_df = pd.read_csv(filepath_or_buffer=csv_file, sep=sep)
elec_df = pd.read_csv(filepath_or_buffer=csv_file, sep=sep, skipinitialspace=True)
# Mean of Winter and Summer of 2025 electric prices (Euros/MWh)
elec_mean = (elec_df['Winter 2025'].mean() + elec_df['Summer 2025'].mean())/2
@@ -75,6 +109,10 @@ def calculate_elec_prices(csv_file:str, sep:str=';'):
# Standard variation of Winter and Summer of 2025 electric prices (Euros/MWh)
elec_std = (elec_df['Winter 2025'].std() + elec_df['Summer 2025'].std())/2
elec_mean = elec_mean / 1000
elec_std = elec_std / 1000
print(f'Electricity prices:\n - Mean: ${elec_mean}€/Mwh\n - Std: ${elec_std}€/Mwh')
return elec_mean, elec_std
@@ -88,7 +126,7 @@ def generate_capacities(csv_file:str, nb_vehicles:int, seed:int=42, sep:str=';')
capacities = pd.Series(all_capacities).sample(n=nb_vehicles, random_state=seed)
print(f'Capacities of vehicles (kwh): ${capacities}')
return capacities
return capacities.tolist()
def get_power_constants(nb_vehicles:int, nb_consumers:int=67000000):
mean_consumption = (87028 + 46847 + 52374 + 29819)/4 # Mean of consumption in France in 2025 (estimate according to data/grid_capacity.txt)
@@ -99,7 +137,8 @@ def get_power_constants(nb_vehicles:int, nb_consumers:int=67000000):
x_min = -x_max
return a_max, x_max, x_min
# --- EXECUTION FUNCTION ---
def run_scenario(scenario_name, capacities:list, price_mean:float, price_std:float, model_type=None, n:int=20, t:int=30, w:float=0.4, c1:float=0.3, c2:float=0.2, archive_size:int=10, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=48):
A_MAX, X_MAX, X_MIN = get_power_constants(nb_vehicles=nb_vehicles)
@@ -131,10 +170,69 @@ def run_scenario(scenario_name, capacities:list, price_mean:float, price_std:flo
print(f"Finished in {duration:.2f} seconds.")
print(f"Best f2 found: {best_f2:.4f}")
return duration, best_f2
return duration, best_f2, optimizer.archive
import matplotlib.pyplot as plt
import numpy as np
def plot_time_benchmark(nb_particles_list, results_dict):
t_mopso = [item[0] for item in results_dict['MOPSO']]
t_mlp = [item[0] for item in results_dict['MLP']]
t_rf = [item[0] for item in results_dict['RF']]
plt.figure(figsize=(10, 6))
plt.plot(nb_particles_list, t_mopso, 'o-', label='Sans IA (MOPSO)', color='#1f77b4', linewidth=2)
plt.plot(nb_particles_list, t_mlp, 's--', label='Avec MLP', color='#ff7f0e', linewidth=2)
plt.plot(nb_particles_list, t_rf, '^-.', label='Avec Random Forest', color='#2ca02c', linewidth=2)
plt.title("Temps d'exécution selon le nombre de particules", fontsize=14, fontweight='bold')
plt.xlabel("Nombre de Particules", fontsize=12)
plt.ylabel("Temps (s)", fontsize=12)
plt.grid(True, linestyle=':', alpha=0.7)
plt.legend(fontsize=11)
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
def plot_f2_benchmark(nb_particles_list, results_dict):
s_mopso = [item[1] for item in results_dict['MOPSO']]
s_mlp = [item[1] for item in results_dict['MLP']]
s_rf = [item[1] for item in results_dict['RF']]
plt.figure(figsize=(10, 6))
plt.plot(nb_particles_list, s_mopso, 'o-', label='Sans IA (MOPSO)', color='#1f77b4', linewidth=2)
plt.plot(nb_particles_list, s_mlp, 's--', label='Avec MLP', color='#ff7f0e', linewidth=2)
plt.plot(nb_particles_list, s_rf, '^-.', label='Avec Random Forest', color='#2ca02c', linewidth=2)
plt.title("Meilleur Score F2 (Convergence) selon le nombre de particules", fontsize=14, fontweight='bold')
plt.xlabel("Nombre de Particules (log scale)", fontsize=12)
plt.ylabel("Meilleur F2 Score", fontsize=12)
plt.grid(True, linestyle=':', alpha=0.7)
plt.legend(fontsize=11)
plt.xscale('log')
plt.tight_layout()
plt.show()
def main():
# --- MAIN ---
if __name__ == "__main__":
# CSV files
elec_price_csv = 'data/elec_prices.csv'
capacity_csv = 'data/vehicle_capacity.csv'
@@ -145,30 +243,70 @@ if __name__ == "__main__":
C1 = 0.3 # Individual trust
C2 = 0.2 # Social trust
ARC_SIZE = 10 # Archive size
nb_vehicle = 20
P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)
CAPACITIES = generate_capacities(capacity_csv, N)
CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle)
NB_TICKS = 48
DELTA = 60
results = {}
results = {
'MOPSO':[],
'MLP': [],
'RF': []
}
nb_particles = [20,50,500,1000,10000]
for k in range(len(nb_particles)):
# 1. Without Surrogate (Baseline)
d1, f1_score, _ = run_scenario(
"Only MOPSO",
capacities=CAPACITIES,
price_mean=P_MEAN,
price_std=P_STD,
nb_vehicles=nb_vehicle, # Important pour la cohérence
model_type=None,
n=nb_particles[k]
)
results['MOPSO'].append((d1, f1_score))
# 2. With MLP
d2, f2_score, _ = run_scenario(
"With MLP",
capacities=CAPACITIES,
price_mean=P_MEAN,
price_std=P_STD,
nb_vehicles=nb_vehicle,
model_type='mlp',
n=nb_particles[k]
)
results['MLP'].append((d2, f2_score))
# 3. With Random Forest
d3, f3_score, _ = run_scenario(
"With Random Forest",
capacities=CAPACITIES,
price_mean=P_MEAN,
price_std=P_STD,
nb_vehicles=nb_vehicle,
model_type='rf',
n=nb_particles[k]
)
results['RF'].append((d3, f3_score))
# --- DISPLAY RESULTS ---
# 1. Without Surrogate (Baseline)
d1, f1_score = run_scenario("No AI", model_type=None)
results['No-AI'] = (d1, f1_score)
# 2. With MLP
d2, f2_score = run_scenario("With MLP", model_type='mlp')
results['MLP'] = (d2, f2_score)
# 3. With Random Forest
d3, f3_score = run_scenario("With Random Forest", model_type='rf')
results['RF'] = (d3, f3_score)
# --- DISPLAY RESULTS ---
print("\n=== SUMMARY ===")
print(f"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}")
print("-" * 45)
for k, v in results.items():
print(f"{k:<15} | {v[0]:<10.2f} | {v[1]:<10.4f}")
for i in range(len(nb_particles)):
print(f"{k:<15}_{nb_particles[i]:<15} | {v[i][0]:<10.2f} | {v[i][1]:<10.4f}")
plot_time_benchmark(nb_particles, results)
plot_f2_benchmark(nb_particles, results)
main()