Main Modifications

.gitignore

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

README.md

@@ -11,6 +11,8 @@ Pour les datasets, nous avons pris diverses sources pour concevoir notre propre
 ## 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.
+
 **Linux:**
 ```bash
 # Installation de UV

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

main.py

@@ -1,4 +1,5 @@
 import time
+import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import copy
@@ -6,7 +7,6 @@ from mopso import MOPSO
 from surrogate_handler import SurrogateHandler
 
 # --- EXTENDED CLASS (Inheritance) ---
-
 class SmartMOPSO(MOPSO):
     def __init__(self, model_type=None, **kwargs):
         super().__init__(**kwargs)
@@ -66,14 +66,60 @@ 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)
+
+    # 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
+
+    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
+
 # --- EXECUTION FUNCTION ---
 def run_scenario(scenario_name, model_type=None):
+    elec_price_csv = 'data/elec_prices.csv'
+    capacity_csv = 'data/vehicle_capacity.csv'
+
+    # Simulation parameters
+    N = 20 # Number of vehicles
+    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
+
+    P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)
+    CAPACITIES = generate_capacities(capacity_csv, N)
+
+    NB_TICKS = 48
+    DELTA = 60
+
+    A_MAX = 0
+    X_MAX = 0
+    X_MIN = 0
+
+
     print(f"\n--- Launching Scenario: {scenario_name} ---")
     start_time = time.time()
     
     # Simulation parameters
     params = {
-        'f_weights': [1,1,1], 'A_max': 500, 'price_mean': 0.15, 'price_std': 0.05,
+        'A_max': 500, 'price_mean': 0.15, 'price_std': 0.05,
         'capacities': [50]*10, 'n': 20, 't': 50, 
         'w': 0.4, 'c1': 2.0, 'c2': 2.0,
         'nb_vehicles': 10, 'delta_t': 60, 'nb_of_ticks': 72
@@ -88,8 +134,8 @@ def run_scenario(scenario_name, model_type=None):
     end_time = time.time()
     duration = end_time - start_time
     
-    # Retrieve best f2 (e.g., from archive)
-    best_f2 = min([p.f_current[1] for p in optimizer.archive]) if optimizer.archive else 0
+    # Retrieve best f2 (e.g. from archive)
+    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}")

mopso.py

@@ -3,7 +3,7 @@ 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
@@ -11,7 +11,6 @@ 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
 
@@ -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

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