Main Modifications

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}")