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243
main.py
243
main.py
@@ -6,40 +6,6 @@ from mopso import MOPSO
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from surrogate_handler import SurrogateHandler
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import pandas as pd
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D # Nécessaire pour la 3D
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def plot_pareto_3d(archive, model_type:str):
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fig = plt.figure(figsize=(12, 8))
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ax = fig.add_subplot(111, projection='3d')
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# Extraction des scores depuis l'archive
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# f_best[0] = Coût, f_best[1] = Insatisfaction, f_best[2] = Stress Réseau
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f1 = [p.f_best[0] for p in archive]
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f2 = [p.f_best[1] for p in archive]
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f3 = [p.f_best[2] for p in archive]
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# Création du nuage de points
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img = ax.scatter(f1, f2, f3, c=f3, cmap='viridis', s=60, edgecolors='black')
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ax.set_xlabel('Coût (€)')
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ax.set_ylabel('Insatisfaction (SoC manquant)')
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ax.set_zlabel('Pic Réseau (kW)')
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ax.set_title(f'Front de Pareto des Solutions Non-Dominées ({model_type})')
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# Barre de couleur
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cbar = fig.colorbar(img, ax=ax, pad=0.1)
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cbar.set_label('Intensité du Pic Réseau (kW)')
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# Sauvegarde et affichage
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filename = f"{model_type}_pareto_3d.png"
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plt.savefig(filename)
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print(f"Graphique sauvegardé sous : {filename}")
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plt.show()
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class SmartMOPSO(MOPSO):
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def __init__(self, model_type=None, **kwargs):
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super().__init__(**kwargs)
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@@ -53,18 +19,7 @@ class SmartMOPSO(MOPSO):
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for p in self.particles:
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self.surrogate_handler.add_data(p.x, p.f_current[1])
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def iterate(self):
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train_freq = 10 # Retrain every 10 iterations
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# Check if retraining is needed
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if self.use_surrogate and (self.t % train_freq == 0):
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self.surrogate_handler.train()
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# Determine if AI prediction should be used
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use_ai = (self.use_surrogate and
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self.surrogate_handler.is_trained and
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self.t % train_freq != 0)
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def iterate(self, prediction_freq:int=10):
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# Main loop (overriding original logic to manage control flow)
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for t in range(self.t):
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self.select_leader()
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@@ -75,7 +30,7 @@ class SmartMOPSO(MOPSO):
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self.particles[i].update_position()
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self.particles[i].keep_boudaries(self.A_max)
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if use_ai:
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if (t % (prediction_freq) != 0) and self.use_surrogate:
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# Fast exact calculation (f1, f3)
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f1 = self.particles[i].f1(self.prices)
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f3 = self.particles[i].f3()
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@@ -90,16 +45,35 @@ class SmartMOPSO(MOPSO):
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# Standard Calculation (Slow and Exact)
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self.particles[i].updating_socs(self.socs, self.capacities)
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self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times)
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# Capture data for AI training
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if self.use_surrogate:
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self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1])
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self.particles[i].update_best()
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self.update_archive()
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# Run Classic MOPSO, collect data and run training for the model
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def train_surrogate_model(self):
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# Generation of data
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for t in range(self.t):
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self.select_leader()
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for i in range(self.n):
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# Movement
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self.particles[i].update_velocity(self.leader.x, self.c1, self.c2, self.w)
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self.particles[i].update_position()
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self.particles[i].keep_boudaries(self.A_max)
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# Standard Calculation (Slow and Exact)
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self.particles[i].updating_socs(self.socs, self.capacities)
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self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times)
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# Capture data for AI training
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self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1])
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# End of dataset generation (based on classic MOPSO)
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self.surrogate_handler.train()
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def calculate_elec_prices(csv_file:str, sep:str=';'):
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elec_df = pd.read_csv(filepath_or_buffer=csv_file, sep=sep, skipinitialspace=True)
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@@ -153,8 +127,11 @@ def run_scenario(scenario_name, capacities:list, price_mean:float, price_std:flo
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'x_min':X_MIN, 'x_max':X_MAX
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}
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# Instantiate extended class
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# Instantiate extended class
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optimizer = SmartMOPSO(model_type=model_type, **params)
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if(model_type is not None):
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optimizer.train_surrogate_model()
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start_time = time.time()
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@@ -174,6 +151,79 @@ def run_scenario(scenario_name, capacities:list, price_mean:float, price_std:flo
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# CSV files
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elec_price_csv = 'data/elec_prices.csv'
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capacity_csv = 'data/vehicle_capacity.csv'
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# Global Simulation parameters
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T = 30 # Number of iterations (for the particles)
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W = 0.4 # Inertia (for exploration)
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C1 = 0.3 # Individual trust
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C2 = 0.2 # Social trust
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ARC_SIZE = 10 # Archive size
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nb_vehicle = 20
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P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)
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CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle)
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NB_TICKS = 48
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DELTA = 60
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results = {
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'MOPSO':[],
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'MLP': [],
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'RF': []
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}
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nb_particles = [20,50,100,500]
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for k in range(len(nb_particles)):
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# 1. Without Surrogate (Baseline)
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d1, f1_score, _ = run_scenario(
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"Only MOPSO",
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capacities=CAPACITIES,
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price_mean=P_MEAN,
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price_std=P_STD,
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nb_vehicles=nb_vehicle, # Important pour la cohérence
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model_type=None,
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n=nb_particles[k]
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)
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results['MOPSO'].append((d1, f1_score))
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# 2. With MLP
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d2, f2_score, _ = run_scenario(
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"With MLP",
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capacities=CAPACITIES,
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price_mean=P_MEAN,
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price_std=P_STD,
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nb_vehicles=nb_vehicle,
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model_type='mlp',
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n=nb_particles[k]
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)
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results['MLP'].append((d2, f2_score))
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# 3. With Random Forest
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d3, f3_score, _ = run_scenario(
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"With Random Forest",
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capacities=CAPACITIES,
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price_mean=P_MEAN,
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price_std=P_STD,
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nb_vehicles=nb_vehicle,
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model_type='rf',
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n=nb_particles[k]
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)
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results['RF'].append((d3, f3_score))
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# --- DISPLAY RESULTS ---
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print("\n=== SUMMARY ===")
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print(f"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}")
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print("-" * 45)
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for k, v in results.items():
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for i in range(len(nb_particles)):
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print(f"{k:<15}_{nb_particles[i]:<15} | {v[i][0]:<10.2f} | {v[i][1]:<10.4f}")
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import matplotlib.pyplot as plt
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import numpy as np
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@@ -200,6 +250,10 @@ def plot_time_benchmark(nb_particles_list, results_dict):
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plt.tight_layout()
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plt.show()
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plot_time_benchmark(nb_particles, results)
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import matplotlib.pyplot as plt
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@@ -226,87 +280,4 @@ def plot_f2_benchmark(nb_particles_list, results_dict):
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plt.tight_layout()
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plt.show()
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def main():
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# CSV files
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elec_price_csv = 'data/elec_prices.csv'
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capacity_csv = 'data/vehicle_capacity.csv'
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# Global Simulation parameters
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T = 30 # Number of iterations (for the particles)
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W = 0.4 # Inertia (for exploration)
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C1 = 0.3 # Individual trust
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C2 = 0.2 # Social trust
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ARC_SIZE = 10 # Archive size
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nb_vehicle = 20
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P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)
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CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle)
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NB_TICKS = 48
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DELTA = 60
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results = {
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'MOPSO':[],
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'MLP': [],
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'RF': []
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}
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nb_particles = [20,50,500,1000,10000]
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for k in range(len(nb_particles)):
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# 1. Without Surrogate (Baseline)
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d1, f1_score, _ = run_scenario(
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"Only MOPSO",
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capacities=CAPACITIES,
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price_mean=P_MEAN,
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price_std=P_STD,
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nb_vehicles=nb_vehicle, # Important pour la cohérence
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model_type=None,
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n=nb_particles[k]
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)
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results['MOPSO'].append((d1, f1_score))
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# 2. With MLP
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d2, f2_score, _ = run_scenario(
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"With MLP",
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capacities=CAPACITIES,
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price_mean=P_MEAN,
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price_std=P_STD,
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nb_vehicles=nb_vehicle,
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model_type='mlp',
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n=nb_particles[k]
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)
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results['MLP'].append((d2, f2_score))
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# 3. With Random Forest
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d3, f3_score, _ = run_scenario(
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"With Random Forest",
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capacities=CAPACITIES,
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price_mean=P_MEAN,
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price_std=P_STD,
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nb_vehicles=nb_vehicle,
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model_type='rf',
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n=nb_particles[k]
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)
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results['RF'].append((d3, f3_score))
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# --- DISPLAY RESULTS ---
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print("\n=== SUMMARY ===")
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print(f"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}")
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print("-" * 45)
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for k, v in results.items():
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for i in range(len(nb_particles)):
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print(f"{k:<15}_{nb_particles[i]:<15} | {v[i][0]:<10.2f} | {v[i][1]:<10.4f}")
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plot_time_benchmark(nb_particles, results)
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plot_f2_benchmark(nb_particles, results)
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main()
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plot_f2_benchmark(nb_particles, results)
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