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MOPSO transition

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KuMiShi
2026-01-15 20:33:19 +01:00
parent a6f84df680
commit 18c9fa43c3
2 changed files with 63 additions and 32 deletions
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63
mopso.py
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@@ -1,20 +1,28 @@
import random as rd
from .particle import Particle
class MOPSO():
def __init__(self, n, t, w, c1, c2, a_max, surrogate=False):
def __init__(self, f_weigths:list, A_max:float, price_mean:float, price_std:float, 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 iterations
self.t = t # Number of simulation iterations
self.w = w # Inertia (for exploration)
self.c1 = c1 # Individual trust
self.c2 = c2 # Social trust
self.a_max = a_max # Archive size
self.archive_size = archive_size # Archive size
self.f_weigths = f_weigths # Weigths for aggregation of all function objective
self.surrogate = surrogate # Using AI calculation
self.particles = [] # Particles of the simulation
# Fonctions objectifs
# Limites variables de decision
# 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.prices = self.generates_prices(price_mean,price_std) #TODO: Use RTE France prices for random prices generation according to number of ticks
# Particles of the simulation
self.particles = [Particle(times=self.times,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 = []
def iterate(self):
nb_iter = 0
@@ -35,4 +43,45 @@ class MOPSO():
# Checking boundaries
# Evaluating particles
# Update the archive
# Checking for best positions
# Checking for best positions
# Generation of arriving and leaving times for every vehicle
def generate_times(self, nb_vehicles, nb_of_ticks, delta_t):
times = []
for _ in range(nb_vehicles):
# Minumun, we have one tick of charging/discharging during simulation
t_arrived = rd.randrange(0, (nb_of_ticks * delta_t - delta_t) +1, delta_t)
t_leaving = rd.randrange(t_arrived + delta_t, (nb_of_ticks * delta_t) +1, delta_t)
times.append((t_arrived,t_leaving))
return times
# Genrates different prices in [mean - std, mean + std] range
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
prices.append(mean + variation)
return prices
# 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 = []
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 _ in range(nb_of_ticks):
socs.append(soc_init/100)
# Adding the requested state of charge
socs_req.append(soc_req/100)
return socs, socs_req
# Random uniform selection for a leader
def select_leader(self):
n = len(self.archive) # Archive length
rd_pos = rd.randrange(0, n, 1)
return self.archive[rd_pos]

32
particle.py
View File

@@ -1,7 +1,7 @@
import random as rd
class Particle():
def __init__(self, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=72, a_min=-100, a_max=100, alpha=0.1):
def __init__(self, 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
@@ -9,12 +9,11 @@ class Particle():
self.socs= self.generate_state_of_charges() # States of charge (initial, requested)
#TODO: Move that to MOPSO (using one batch of times for multiples particles)
self.times = self.generate_times() # Times (arrived, leaving)
self.times = times # (arrived, leaving)
# Minima and maxima of a position value
self.a_min = a_min
self.a_max = a_max
self.x_min = x_min
self.x_max = x_max
# Limitation of the velocity
self.alpha = alpha
@@ -36,24 +35,6 @@ class Particle():
for i in range(self.nb_vehicles):
new_vel_i = w * self.v[i] + (self.p_best - self.x[i]) * c1 * self.r1[i] + (leader - self.x[i]) * c2 * self.r2[i]
self.v[i] = new_vel_i
def generate_state_of_charges(self):
socs = []
# We ensure soc_req is greater than what the soc_init is (percentage transformed into floats)
for _ in range(self.nb_vehicles):
soc_init = rd.randrange(0,100,1)
soc_req = rd.randrange(soc_init+1, 101,1)
socs.append((soc_init/100, soc_req/100))
return socs
def generate_times(self):
times = []
for _ in range(self.nb_vehicles):
# Minumun, we have one tick of charging during simulation
t_arrived = rd.randrange(0, (self.nb_of_ticks * self.delta_t - self.delta_t) +1, self.delta_t)
t_leaving = rd.randrange(t_arrived + self.delta_t, (self.nb_of_ticks*self.delta_t)+1, self.delta_t)
times.append((t_arrived,t_leaving))
return times
#TODO: Modify for uses of ticks
def generate_position(self):
@@ -61,13 +42,14 @@ class Particle():
for _ in range(self.nb_of_ticks):
x_tick = []
for _ in range(self.nb_vehicles):
x_tick.append(rd.randrange(self.a_min, self.a_max +1, 1))
x_tick.append(rd.randrange(self.x_min, self.x_max +1, 1))
pos.append(x_tick)
return pos
# Randomize a velocity vector for each tick
def generate_velocity(self):
vel = []
vel_coeff = self.a_max - self.a_min
vel_coeff = abs(self.x_max - self.x_min)
for _ in range(self.nb_of_ticks):
v_tick = []
for _ in range(self.nb_vehicles):