Update Report

.gitignore

@@ -1,10 +1,3 @@
-# Scripts
-main.py
-
 # UV Environment
 .python-version
 .venv
-
-# Datasets
-dataset.py
-data/capacity.csv

README.md

@@ -1,15 +1,18 @@
 # Mini Projet - Optimisation Métaheuristique
 
-Ceci est le répertoire Git du projet d'optimisation métaheuristique du groupe 9 dont les membres sont AIT MOUSSA Amine, DAANOUNI Siham et DELAMOTTE Clément.
+Ceci est le répertoire Git du projet d'optimisation métaheuristique du groupe 9 dont les membres sont **AIT MOUSSA Amine, DAANOUNI Siham et DELAMOTTE Clément**.
 
-Le sujet choisi est **l'optimisation du chargement des véhicules électriques** et l'algorithme mis en place est **Multiple Objectives Particle Swarm Optimization (MOPSO) + Surrogate**. La modélisation du problème se trouvera dans le rapport.
+Le sujet choisi est **l'optimisation du chargement des véhicules électriques** et l'algorithme mis en place est **Multiple Objectives Particle Swarm Optimization (MOPSO) + Surrogate**. La modélisation du problème se trouvera dans le rapport et les slides de présentation.
 
-Pour les datasets, nous avons pris diverses sources pour concevoir notre propre jeu de données:
+Pour les datasets, nous avons pris diverses sources réalistes pour concevoir nos propres jeux de données afin de pouvoir récupérer des paramètres cruciaux:
-- data/vehicle_capacity.csv: [Car Dataset (2025)](https://www.kaggle.com/datasets/abdulmalik1518/cars-datasets-2025/data)
+- data/vehicle_capacity.csv: [Car Dataset (2025)](https://www.kaggle.com/datasets/abdulmalik1518/cars-datasets-2025/data).
-- data/elec_prices.csv: [RTE France (éco2mix)](https://www.rte-france.com/donnees-publications/eco2mix-donnees-temps-reel/donnees-marche), les données ont été récupérées manuellement sur l'hivers 2025 (S2-S5) et l'été 2025 (S29-S32)
+- data/elec_prices.csv: [RTE France (éco2mix)](https://www.rte-france.com/donnees-publications/eco2mix-donnees-temps-reel/donnees-marche), les données ont été récupérées manuellement sur l'hivers 2025 (S2-S5) et l'été 2025 (S29-S32).
+- data/grid_capacity.txt: [RTE France (éco2mix)](https://www.rte-france.com/donnees-publications/eco2mix-donnees-temps-reel/donnees-marche), même procédé qu'au dessus.
 
 ## Installation
-Le projet a été concu à l'aide du *Python packet manager* ***UV***, il est préférable d'utiliser celui-ci pour ca facilité d'utilisation. **UV** peut être installé via le [site internet officiel](https://docs.astral.sh/uv/getting-started/installation/#installing-uv).
+Pour télécharger le projet vous pouvez simplement utiliser la commande `git clone https://gitea.galaxynoliro.fr/KuMiShi/Optim_Metaheuristique.git` ou récupérer le fichier `.zip` du projet et l'extraire.
+
+Le projet a été concu à l'aide du ***Python packet manager UV***, il est préférable d'utiliser celui-ci pour sa facilité d'utilisation **sauf si vous vous contentez de regarder les résultats de notre notebook**. **UV** peut être installé via le [site internet officiel](https://docs.astral.sh/uv/getting-started/installation/#installing-uv) sur tout système d'exploitation.
 
 **Linux:**
 ```bash
@@ -27,6 +30,11 @@ winget install --id=astral-sh.uv  -e
 ```
 
 ## Utilisation
+Vous pouvez utiliser le projet de deux manières:
+
+1. Récupérer le notebook et suivre les cellules une à une avec les résultats pré-compiler dans le fichier.
+2. Exécuter le projet complet à l'aide du code source et de **UV**
+
 Pour charger le projet et l'executer sans problème, il faut d'abord configurer notre environnement d'execution de la manière suivante:
 
 ```bash
@@ -36,8 +44,8 @@ uv venv
 # Téléchargement des requirements du projet
 uv pip sync uv.lock
 
-# Si uv.lock n'existe pas, vous pouvez le générer avec la commande suivante:
+# Si uv.lock ne fonctionne pas correctement ou n'existe pas, vous pouvez le générer avec la commande suivante à partir du .toml:
 uv pip compile --upgrade pyproject.toml -o uv.lock
 ```
 
-Enfin, vous pouvez executer n'importe quel script avec la commande `uv run main.py` (main.py pouvant etre remplacé par n'importe quel autre script python executable).
+Enfin, vous pouvez executer n'importe quel script avec la commande `uv run main.py`, sachant que `main.py` peut être remplacé par n'importe quel autre script python executable.

data/elec_prices.csv

@@ -14,7 +14,7 @@ Winter 2025; Summer 2025
 12.54; 76.77
 0.4; 63.01
 60.01; 54.1
-1158; 69.52
+115.8; 69.52
 93.49; 94.16
 71.25; 30.5
 79.76; 46.2

data/grid_capacity.txt

@@ -0,0 +1,9 @@
+                    |   Maximum     |   Minimum
+---------------------------------------------------
+Consumption (Winter)|   87 028 Mwh  |   46 847 Mwh
+            (Summer)|   52 374 Mwh  |   29 819 Mwh
+---------------------------------------------------
+Production  (Winter)|   91 341 Mwh  |   72 926 Mwh
+            (Summer)|   86 579 Mwh  |   49 127 Mwh
+
+Winter correspond to S2-S5 and Summer correspond to S29-S32 (same as prices)

main.py

@@ -4,8 +4,7 @@ import matplotlib.pyplot as plt
 import copy
 from mopso import MOPSO 
 from surrogate_handler import SurrogateHandler
-
+import pandas as pd
-# --- EXTENDED CLASS (Inheritance) ---
 
 class SmartMOPSO(MOPSO):
     def __init__(self, model_type=None, **kwargs):
@@ -20,18 +19,7 @@ class SmartMOPSO(MOPSO):
             for p in self.particles:
                 self.surrogate_handler.add_data(p.x, p.f_current[1])
 
-    def iterate(self):
+    def iterate(self, prediction_freq:int=10):
-        train_freq = 10 # Retrain every 10 iterations
-        
-        # Check if retraining is needed
-        if self.use_surrogate and (self.t % train_freq == 0):
-            self.surrogate_handler.train()
-
-        # Determine if AI prediction should be used
-        use_ai = (self.use_surrogate and 
-                  self.surrogate_handler.is_trained and 
-                  self.t % train_freq != 0)
-
         # Main loop (overriding original logic to manage control flow)
         for t in range(self.t): 
             self.select_leader()
@@ -42,79 +30,254 @@ class SmartMOPSO(MOPSO):
                 self.particles[i].update_position()
                 self.particles[i].keep_boudaries(self.A_max)
 
-                if use_ai:
+                if (t % (prediction_freq) != 0) and self.use_surrogate:
-                    # 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)
 
-                    # Capture data for AI training
-                    if self.use_surrogate:
-                        self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1])
-
                 self.particles[i].update_best()
             
             self.update_archive()
 
-# --- EXECUTION FUNCTION ---
-def run_scenario(scenario_name, model_type=None):
-    print(f"\n--- Launching Scenario: {scenario_name} ---")
-    start_time = time.time()
 
+    # Run Classic MOPSO, collect data and run training for the model
+    def train_surrogate_model(self):
+        # Generation of data
+        for t in range(self.t): 
+            self.select_leader()
+            
+            for i in range(self.n):
+                # Movement 
+                self.particles[i].update_velocity(self.leader.x, self.c1, self.c2, self.w)
+                self.particles[i].update_position()
+                self.particles[i].keep_boudaries(self.A_max)
+                # 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)
+                    
+                # Capture data for AI training
+                self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1])
+        
+        # End of dataset generation (based on classic MOPSO)
+        self.surrogate_handler.train()
+
+
+
+def calculate_elec_prices(csv_file:str, sep:str=';'):
+    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
+
+    # 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
+
+def generate_capacities(csv_file:str, nb_vehicles:int, seed:int=42, sep:str=';'):
+    cap_df = pd.read_csv(filepath_or_buffer=csv_file, sep=sep)
+
+    # Getting back all kind of battery capacities with unique values
+    all_capacities = cap_df['Battery Capacity kwh'].dropna().unique()
+
+    # Extracting random values for generating the array of capacities
+    capacities = pd.Series(all_capacities).sample(n=nb_vehicles, random_state=seed)
+
+    print(f'Capacities of vehicles (kwh): ${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)
+    sim_ratio = nb_vehicles / nb_consumers # Ratio to reduce A_max of simulation to realistic restrictions
+
+    a_max = sim_ratio * mean_consumption
+    x_max = a_max / nb_vehicles # For init, uniform charging/discharging for every vehicle
+    x_min = -x_max
+    return a_max, x_max, x_min
+
+
+
+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)
+
+
+    print(f"\n--- Launching Scenario: {scenario_name} ---")
     # Simulation parameters
     params = {
-        'f_weights': [1,1,1], 'A_max': 500, 'price_mean': 0.15, 'price_std': 0.05,
+        'A_max': A_MAX, 'price_mean': price_mean, 'price_std': price_std,
-        'capacities': [50]*10, 'n': 20, 't': 50, 
+        'capacities': capacities, 'n': n, 't': t, 
-        'w': 0.4, 'c1': 2.0, 'c2': 2.0,
+        'w': w, 'c1': c1, 'c2': c2,
-        'nb_vehicles': 10, 'delta_t': 60, 'nb_of_ticks': 72
+        'nb_vehicles': nb_vehicles, 'delta_t': delta_t, 'nb_of_ticks': nb_of_ticks,
+        'x_min':X_MIN, 'x_max':X_MAX
     }
     
 # Instantiate extended class
     optimizer = SmartMOPSO(model_type=model_type, **params)
 
+    if(model_type is not None):
+        optimizer.train_surrogate_model()
+    
+    start_time = time.time()
+    
     # Run simulation
     optimizer.iterate()
         
     end_time = time.time()
     duration = end_time - start_time
     
-    # Retrieve best f2 (e.g., from archive)
+    # Retrieve best f2 (e.g. from archive)
-    best_f2 = min([p.f_current[1] for p in optimizer.archive]) if optimizer.archive else 0
+    best_f2 = min([p.f_best[1] for p in optimizer.archive]) if optimizer.archive else 0
     
     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
 
-# --- MAIN ---
-if __name__ == "__main__":
-    results = {}
 
+
+
+
+# CSV files
+elec_price_csv = 'data/elec_prices.csv'
+capacity_csv = 'data/vehicle_capacity.csv'
+
+# Global Simulation parameters
+T = 30 # Number of iterations (for the particles)
+W = 0.4 # Inertia (for exploration)
+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, nb_vehicles=nb_vehicle)
+
+NB_TICKS = 48
+DELTA = 60
+
+results = {
+    'MOPSO':[],
+    'MLP': [],
+    'RF': []
+}
+nb_particles = [20,50,100,500]
+
+for k in range(len(nb_particles)):
     # 1. Without Surrogate (Baseline)
-    d1, f1_score = run_scenario("No AI", model_type=None)
+    d1, f1_score, _ = run_scenario(
-    results['No-AI'] = (d1, f1_score)
+        "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", model_type='mlp')
+    d2, f2_score, _ = run_scenario(
-    results['MLP'] = (d2, f2_score)
+        "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", model_type='rf')
+    d3, f3_score, _ = run_scenario(
-    results['RF'] = (d3, f3_score)
+        "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 ---
 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}")
+
+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()
+
+    
+plot_time_benchmark(nb_particles, results)
+
+
+
+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()
+
+plot_f2_benchmark(nb_particles, results)

mopso.py

@@ -1,9 +1,9 @@
 import random as rd
-from .particle import Particle
+from particle import Particle
 import copy
 
 class MOPSO():
-    def __init__(self, f_weights:list, A_max:float, price_mean:float, price_std:float, capacities:list, n:int, t:int, w:float, c1:float, c2:float, archive_size:int=10, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=72, x_min=-100, x_max=100, v_alpha=0.1, surrogate=False):
+    def __init__(self, A_max:float, price_mean:float, price_std:float, capacities:list, n:int, t:int, w:float, c1:float, c2:float, archive_size:int=10, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=72, x_min=-100, x_max=100, v_alpha=0.1, surrogate=False):
         # Constants
         self.n = n # Number of particles
         self.t = t # Number of simulation iterations
@@ -11,14 +11,13 @@ class MOPSO():
         self.c1 = c1 # Individual trust
         self.c2 = c2 # Social trust
         self.archive_size = archive_size # Archive size
-        self.f_weights = f_weights # Weigths for aggregation of all function objective
 
         self.surrogate = surrogate # Using AI calculation
 
         # Initialisation of particle's global parameters
         self.A_max = A_max # Network's power limit
         self.socs, self.socs_req = self.generate_state_of_charges(nb_vehicles,nb_of_ticks)
-        self.times = self.generate_times(nb_vehicles, nb_of_ticks, delta_t)
+        self.times = self.generate_times(nb_vehicles, nb_of_ticks)
         self.prices = self.generates_prices(nb_of_ticks,price_mean,price_std) #TODO: Use RTE France prices for random prices generation according to number of ticks
         self.capacities = capacities
 
@@ -71,7 +70,7 @@ class MOPSO():
                 # Checking for best positions
     
     # Generation of arriving and leaving times for every vehicle
-    def generate_times(self, nb_vehicles, nb_of_ticks, delta_t):
+    def generate_times(self, nb_vehicles, nb_of_ticks):
         times = []
         for _ in range(nb_vehicles):
             # Minumun, we have one tick of charging/discharging during simulation
@@ -84,7 +83,7 @@ class MOPSO():
     def generates_prices(self,nb_of_ticks:int, mean:float, std:float):
         prices = []
         for _ in range(nb_of_ticks):
-            variation = rd.randrange(-(std*10), (std * 10) +1, 1) / 10 # Random float variation
+            variation = rd.uniform(-std, std) # Random float variation
             prices.append(mean + variation)
         return prices
     

particle.py

@@ -65,14 +65,12 @@ class Particle():
                         self.x[tick][i] = self.x[tick][i] * 0.9
                 current_power = self.get_current_grid_stress(tick)
 
-
-
     def generate_position(self):
         pos = []
         for _ in range(self.nb_of_ticks):
             x_tick = []
             for _ in range(self.nb_vehicles):
-                x_tick.append(rd.randrange(self.x_min, self.x_max +1, 1))
+                x_tick.append(rd.uniform(self.x_min, self.x_max))
             pos.append(x_tick)
         return pos
     
@@ -83,7 +81,8 @@ class Particle():
         for _ in range(self.nb_of_ticks):
             v_tick = []
             for _ in range(self.nb_vehicles):
-                v_tick.append(rd.randrange(-vel_coeff, vel_coeff +1, 1) * self.alpha)
+                # v_tick.append(rd.randrange(-vel_coeff, vel_coeff +1, 1) * self.alpha)
+                v_tick.append(rd.uniform(-vel_coeff, vel_coeff) * self.alpha)
             vel.append(v_tick)
         return vel
     

pyproject.toml

@@ -5,5 +5,8 @@ description = "Metaheuristic Optimization Project"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
+    "matplotlib>=3.10.8",
+    "numpy>=2.4.1",
     "pandas>=2.3.3",
+    "scikit-learn>=1.8.0",
 ]

uv.lock

@@ -1,14 +1,46 @@
 # This file was autogenerated by uv via the following command:
 #    uv pip compile pyproject.toml -o uv.lock
+contourpy==1.3.3
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+fonttools==4.61.1
+    # via matplotlib
+joblib==1.5.3
+    # via scikit-learn
+kiwisolver==1.4.9
+    # via matplotlib
+matplotlib==3.10.8
+    # via optim-meta (pyproject.toml)
 numpy==2.4.1
-    # via pandas
+    # via
+    #   optim-meta (pyproject.toml)
+    #   contourpy
+    #   matplotlib
+    #   pandas
+    #   scikit-learn
+    #   scipy
+packaging==25.0
+    # via matplotlib
 pandas==2.3.3
     # via optim-meta (pyproject.toml)
+pillow==12.1.0
+    # via matplotlib
+pyparsing==3.3.1
+    # via matplotlib
 python-dateutil==2.9.0.post0
-    # via pandas
+    # via
+    #   matplotlib
+    #   pandas
 pytz==2025.2
     # via pandas
+scikit-learn==1.8.0
+    # via optim-meta (pyproject.toml)
+scipy==1.17.0
+    # via scikit-learn
 six==1.17.0
     # via python-dateutil
+threadpoolctl==3.6.0
+    # via scikit-learn
 tzdata==2025.3
     # via pandas