forked from KuMiShi/Optim_Metaheuristique
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
import random as rd
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class Particle():
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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):
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# Problem specific attributes
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self.nb_vehicles = nb_vehicles # Number of vehicles handles for the generations of position x
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self.delta_t = delta_t # delta_t for update purposes
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self.nb_of_ticks = nb_of_ticks # Accounting for time evolution of the solution (multiplied by delta_t)
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self.socs= self.generate_state_of_charges() # States of charge (initial, requested)
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self.times = times # (arrived, leaving)
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# Minima and maxima of a position value
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self.x_min = x_min
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self.x_max = x_max
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# Limitation of the velocity
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self.alpha = alpha
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self.r1 = [rd.randrange(0,101,1)/100 for _ in range(self.nb_vehicles)] # Variable trust of oneself
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self.r2 = [rd.randrange(0,101,1)/100 for _ in range(self.nb_vehicles)] # Variable trust of other particles
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# Particle attributes
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self.x = self.generate_position() # Position Vector (correspond to one solution for the problem)
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self.v = self.generate_velocity() # Velocity
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self.p_best = self.x # Best known position (starting with initial position x)
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self.eval = 0 #TODO: self.evaluate()
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def update_position(self):
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for i in range(self.nb_vehicles):
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new_pos_i = self.x[i] + self.v[i]
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self.x[i] = new_pos_i
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def update_velocity(self, leader, c1, c2, w=0.4):
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for i in range(self.nb_vehicles):
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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]
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self.v[i] = new_vel_i
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#TODO: Modify for uses of ticks
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def generate_position(self):
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pos = []
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for _ in range(self.nb_of_ticks):
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x_tick = []
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for _ in range(self.nb_vehicles):
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x_tick.append(rd.randrange(self.x_min, self.x_max +1, 1))
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pos.append(x_tick)
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return pos
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# Randomize a velocity vector for each tick
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def generate_velocity(self):
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vel = []
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vel_coeff = abs(self.x_max - self.x_min)
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for _ in range(self.nb_of_ticks):
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v_tick = []
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for _ in range(self.nb_vehicles):
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v_tick.append(rd.randrange(-vel_coeff, vel_coeff +1, 1) * self.alpha)
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vel.append(v_tick)
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return vel
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# Function objective
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def evaluate(self, elec_prices, max_power):
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pass
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# Calculate the price of the electricity consumption in the grid SUM(1_to_T)(Epsilon_t * A_t * delta_t)
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def f1(self, elec_prices):
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result = 0
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for tick in range(self.nb_of_ticks):
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grid_stress_tick = self.get_current_grid_stress(tick)
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result += elec_prices[tick] * grid_stress_tick * self.delta_t
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return result
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#TODO: Modify for uses of ticks
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# User's insatisfaction
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def f2(self):
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result = 0
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for i in range(self.nb_vehicles):
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soc_req_i = self.socs[i][1]
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result += max(0, )
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# Network Stress
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def f3(self):
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current_max = 0
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for tick in range(self.nb_of_ticks):
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current_max = max(current_max, self.get_current_grid_stress(tick))
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return current_max
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#TODO: Modify for uses of ticks
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def get_current_grid_stress(self, tick:int):
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assert tick < self.nb_of_ticks # Make sure the tick exist in the position x
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current_grid_stress = 0
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for i in range(self.nb_vehicles):
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current_grid_stress += self.x[tick][i]
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return current_grid_stress |