Optim_Metaheuristique/main.py

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

# --- EXTENDED CLASS (Inheritance) ---
class SmartMOPSO(MOPSO):
    def __init__(self, model_type=None, **kwargs):
        super().__init__(**kwargs)
        
        # Initialize Surrogate Handler if model_type is provided
        self.use_surrogate = (model_type is not None)
        if self.use_surrogate:
            self.surrogate_handler = SurrogateHandler(model_type)

            # Pre-fill with initial particle data
            for p in self.particles:
                self.surrogate_handler.add_data(p.x, p.f_current[1])

    def iterate(self):
        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()
            
            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)

                if use_ai:
                    # 1. Fast exact calculation (f1, f3)
                    f1 = self.particles[i].f1(self.prices)
                    f3 = self.particles[i].f3()
                    
                    # 2. Slow prediction (f2) via AI
                    f2_pred = self.surrogate_handler.predict(self.particles[i].x)
                    
                    # 3. Inject scores without running the expensive 'updating_socs'
                    self.particles[i].f_current = [f1, f2_pred, f3]
                    
                else:
                    # Standard Calculation (Slow & 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()

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 = {
        '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
    }
    
    # Instantiate extended class
    optimizer = SmartMOPSO(model_type=model_type, **params)
    
    # Run simulation
    optimizer.iterate()
        
    end_time = time.time()
    duration = end_time - start_time
    
    # 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}")
    
    return duration, best_f2

# --- MAIN ---
if __name__ == "__main__":
    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}")