66 lines
2.9 KiB
Python
66 lines
2.9 KiB
Python
import random as rd
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class Particle():
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def __init__(self, nb_vehicles:int=10, delta_t:int=60, sim_duration:int=4320, a_min=-100, a_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.sim_duration = sim_duration # max duration and number of updates (multiplied by delta_time)
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self.socs= self.generate_state_of_charges() # States of charge (initial, requested)
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self.times = self.generate_times() # Times (arrived, leaving)
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# Minima and maxima of a position value
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self.a_min = a_min
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self.a_max = a_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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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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def generate_state_of_charges(self):
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socs = []
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# We ensure soc_req is greater than what the soc_init is (percentage transformed into floats)
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for _ in range(self.nb_vehicles):
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soc_init = rd.randrange(0,100,1)
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soc_req = rd.randrange(soc_init+1, 101,1)
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socs.append((soc_init/100, soc_req/100))
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return socs
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def generate_times(self):
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times = []
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for _ in range(self.nb_vehicles):
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# Minumun, we have one tick of charging during simulation
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t_arrived = rd.randrange(0, (self.sim_duration - self.delta_time) +1, self.delta_time)
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t_leaving = rd.randrange(t_arrived + self.delta_time, self.sim_duration+1, self.delta_time)
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times.append((t_arrived,t_leaving))
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return times
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def generate_position(self):
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pos = []
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for _ in range(self.nb_vehicles):
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pos.append(rd.randrange(self.a_min, self.a_max +1, 1))
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return pos
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def generate_velocity(self):
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vel = []
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vel_coeff = self.a_max - self.a_min
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for _ in range(self.nb_vehicles):
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vel.append(rd.randrange(-vel_coeff, vel_coeff +1, 1) * self.alpha)
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return vel |