update without blocking errors

main.py

@@ -1,121 +1,6 @@
-import time
+def main():
-import numpy as np
+    print("Hello from optim-meta!")
-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 (unchanged)
-                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)
-
-                # --- MODIFIED PART: EVALUATION ---
-                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()
-
-# --- EXECUTION FUNCTION ---
-def run_scenario(scenario_name, model_type=None):
-    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,
-        '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_current[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 = {}
+    main()
-    
-    # 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}")

mopso.py

@@ -1,5 +1,6 @@
 import random as rd
 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):
@@ -18,16 +19,27 @@ class MOPSO():
         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.prices = self.generates_prices(price_mean,price_std) #TODO: Use RTE France prices for random prices generation according to number 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
 
         # Particles of the simulation
-        self.particles = [Particle(nb_vehicles=nb_vehicles, nb_of_ticks=nb_of_ticks, delta_t=delta_t, x_min=x_min, x_max=x_max, alpha=v_alpha) for _ in range(self.n)]
+        self.particles = [
+            Particle(
+                socs=copy.deepcopy(self.socs), 
+                times=self.times, # Ajouté ici
+                nb_vehicles=nb_vehicles, 
+                nb_of_ticks=nb_of_ticks, 
+                delta_t=delta_t, 
+                x_min=x_min, 
+                x_max=x_max, 
+                alpha=v_alpha
+            ) for _ in range(self.n)
+        ]
         self.archive = []
         self.leader = self.particles[0] # it doesnt matter as the first thing done is choosing a new leader
         
         for i in range(self.n):
-            self.particles[i].evaluate(self.f_weights, self.prices, self.socs, self.socs_req, self.times)
+            self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times)
         self.update_archive()
     
     def iterate(self):
@@ -78,46 +90,48 @@ class MOPSO():
     
     # Genrates the coordinated states of charges requested and initial (duplicated initially for other ticks)
     def generate_state_of_charges(self, nb_vehicles:int, nb_of_ticks:int):
-        socs = []
+        # Structure souhaitée : socs[tick][vehicle] pour être cohérent avec self.x[tick][vehicle]
+        socs = [[0.0 for _ in range(nb_vehicles)] for _ in range(nb_of_ticks)]
         socs_req = []
-        # We ensure soc_req is greater than what the soc_init is (percentage transformed into floats)
-        for _ in range(nb_vehicles):
-            soc_init = rd.randrange(0,100,1)
-            soc_req = rd.randrange(soc_init+1, 101,1)
 
-            # Creating states of charges for each tick in time
+        for i in range(nb_vehicles):
-            for _ in range(nb_of_ticks):
+            soc_init = rd.randrange(0, 100, 1)
-                socs.append(soc_init/100)
+            soc_req = rd.randrange(soc_init + 1, 101, 1)
+
+            # Remplissage de la matrice 2D
+            for tick in range(nb_of_ticks):
+                socs[tick][i] = soc_init / 100.0
+            
+            socs_req.append(soc_req / 100.0)
             
-            # Adding the requested state of charge
-            socs_req.append(soc_req/100)
         return socs, socs_req
     
     # True if a dominates b, else false
-    def dominates(a:Particle, b:Particle):
+    def dominates(self, a:Particle, b:Particle):
-        dominates = (a.f_current[0] >= b.f_current[0]) and (a.f_current[1] >= b.f_current[1]) and (a.f_current[2] >= b.f_current[2])
+        dominates = (a.f_current[0] <= b.f_current[0]) and (a.f_current[1] <= b.f_current[1]) and (a.f_current[2] <= b.f_current[2])
         if dominates:
             # Not strict superiority yet
-            dominates = (a.f_current[0] > b.f_current[0]) or (a.f_current[1] > b.f_current[1]) or (a.f_current[2] > b.f_current[2])
+            dominates = (a.f_current[0] < b.f_current[0]) or (a.f_current[1] < b.f_current[1]) or (a.f_current[2] < b.f_current[2])
         return dominates
 
-
     def update_archive(self):
         candidates = self.archive + self.particles
         length = len(candidates)
 
         non_dominated = []
+
         for i in range(length):
             candidate_i = candidates[i]
-            dominates = True
+            is_dominated = False 
+            
             for j in range(length):
-                if i!=j:
+                if i != j:
                     candidate_j = candidates[j]
-                    dominates = dominates and self.dominates(candidate_i, candidate_j)
+                    if self.dominates(candidate_j, candidate_i):
-            if dominates:
+                        is_dominated = True
+                        break 
+            if not is_dominated:
                 non_dominated.append(candidate_i)
-
-        # Keeping only a certain number of solutions depending on archive_size (to avoid overloading the number of potential directions for particles)
         if len(non_dominated) > self.archive_size:
             final_non_dominated = []
             while len(final_non_dominated) < self.archive_size:

particle.py

@@ -1,12 +1,14 @@
 import random as rd
+import copy
 
 class Particle():
-    def __init__(self,socs:list, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=72, x_min=-100, x_max=100, alpha=0.1):
+    def __init__(self, socs:list, times:list, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=72, x_min=-100, x_max=100, alpha=0.1):
         # Problem specific attributes
         self.nb_vehicles = nb_vehicles # Number of vehicles handles for the generations of position x
         self.delta_t = delta_t # delta_t for update purposes
         self.nb_of_ticks = nb_of_ticks # Accounting for time evolution of the solution (multiplied by delta_t)
         self.socs = socs # States of charges for the particle current position (self.x)
+        self.times = times
 
         # Minima and maxima of a position value
         self.x_min = x_min
@@ -62,11 +64,8 @@ class Particle():
                     if self.x[tick][i] > 0:
                         self.x[tick][i] = self.x[tick][i] * 0.9
                 current_power = self.get_current_grid_stress(tick)
-    
+                
-    def update_socs(self, capacities):
+
-        for tick in range(self.nb_of_ticks):
-            for i in range(self.nb_vehicles-1):
-                self.socs[tick][i+1] = self.socs[tick][i] + (self.x[tick][i] / capacities[i])
 
     def generate_position(self):
         pos = []
@@ -91,7 +90,7 @@ class Particle():
     # Function objective
     def evaluate(self,elec_prices,socs,socs_req,times):
         f1 = self.f1(elec_prices)
-        f2 = self.f2(socs,socs_req,times)
+        f2 = self.f2(self.socs,socs_req,times)
         f3 = self.f3()
 
         # Keeping in memory evaluation of each objective for domination evaluation
@@ -103,13 +102,13 @@ class Particle():
         self.f_current = f_current
 
     def update_best(self):
-        current_better = (self.f_current[0] >= self.f_best[0]) and (self.f_current[1] >= self.f_best[1]) and (self.f_current[2] >= self.f_best[2])
+        current_better = (self.f_current[0] <= self.f_best[0]) and (self.f_current[1] <= self.f_best[1]) and (self.f_current[2] <= self.f_best[2])
         if current_better:
             # Not strict superiority yet
-            current_dominates = (self.f_current[0] > self.f_best[0]) or (self.f_current[1] > self.f_best[1]) or (self.f_current[2] > self.f_best[2])
+            current_dominates = (self.f_current[0] < self.f_best[0]) or (self.f_current[1] < self.f_best[1]) or (self.f_current[2] < self.f_best[2])
             if current_dominates:
-                self.p_best = self.x
+                self.p_best = copy.deepcopy(self.x)
-                self.f_best = self.f_current
+                self.f_best = self.f_current[:]
 
     # Calculate the price of the electricity consumption in the grid SUM(1_to_T)(Epsilon_t * A_t * delta_t)
     def f1(self,elec_prices):
@@ -142,5 +141,16 @@ class Particle():
             current_grid_stress += self.x[tick][i]
         return current_grid_stress
     
-    def updating_socs(self, socs, capacities):
+    def updating_socs(self, initial_socs, capacities):
-        pass
+    
+        # Calcul de l'évolution temporelle
+        for tick in range(self.nb_of_ticks - 1): # On s'arrête à l'avant-dernier pour calculer le suivant
+            for i in range(self.nb_vehicles):
+                # SoC(t+1) = SoC(t) + (Puissance(t) * delta_t / Capacité)
+                energy_added = (self.x[tick][i] * (self.delta_t / 60)) 
+                
+                # Mise à jour du tick suivant basé sur le tick actuel
+                # On utilise initial_socs comme base si c'est une liste de listes [tick][vehicule]
+                self.socs[tick+1][i] = self.socs[tick][i] + (energy_added / capacities[i])
+                
+                self.socs[tick+1][i] = max(0.0, min(1.0, self.socs[tick+1][i]))