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pso-ras.py
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pso-ras.py
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# -*- coding: cp1252 -*-
#File: pso_wikipedia.py
#Example of PSO based on the wikipedia entry
#Jorge Luis Rosas Trigueros, Ph.D.
#Last modification: 18 oct 2016
from Tkinter import *
from numpy import *
#import random
lower_limit=-20
upper_limit=20
itera=1
n_particles=10
n_dimensions=2
def f(dupla):
x1=1*dupla[0]
x2=1*dupla[1]
res = 0
res = 20;
res += x1*x1 - 10* math.cos(2 * math.pi * x1)
res += x2*x2 - 10* math.cos(2 * math.pi * x2)
return res
# Initialize the particle positions and their velocities
X = lower_limit + (upper_limit - lower_limit) * random.rand(n_particles, n_dimensions)
assert X.shape == (n_particles, n_dimensions)
V = zeros(X.shape)
# Initialize the global and local fitness to the worst possible
fitness_gbest = inf
fitness_lbest = fitness_gbest * ones(n_particles)
X_lbest= 1*X
X_gbest= 1*X_lbest[0]
fitness_X = zeros(X.shape)
for I in range(0, n_particles):
if f(X_lbest[I])<f(X_gbest):
X_gbest=1*X_lbest[I]
def iteracion():
global itera,X,X_lbest,X_gbest,V
# Loop until convergence, in this example a finite number of iterations chosen
w.delete(ALL)
weight=.03
C1=0.3
C2=0.6
print "Best particle in:",X_gbest," gbest: ",f(X_gbest), " iteracion: ",itera
itera+=1
# Update the particle velocity and position
for I in range(0, n_particles):
for J in range(0, n_dimensions):
R1 = random.rand()#uniform_random_number()
R2 = random.rand()#uniform_random_number()
V[I][J] = (weight*V[I][J]
+ C1*R1*(X_lbest[I][J] - X[I][J])
+ C2*R2*(X_gbest[J] - X[I][J]))
X[I][J] = X[I][J] + V[I][J]
if f(X[I])<f(X_lbest[I]):
X_lbest[I]=1*X[I]
if f(X_lbest[I])<f(X_gbest):
X_gbest=1*X_lbest[I]
#graph_f()
#graph_population(X,V,w,s,s,xo,yo,'blue',0.2)
#graph_population([X_gbest],V,w,s,s,xo,yo,'red',0.4)
w.update()
#código para la graficación
master = Tk()
xmax=400
ymax=400
xo=200
yo=200
s=10
w = Canvas(master, width=xmax, height=ymax)
w.pack()
b = Button(master, text="Iniciar", command=iteracion)
b.pack()
N=100
def graph_f():
xini=-20.
xfin=20.
dx=(xfin-xini)/N
xold=xini
yold=f(xold) #evaluate_fitness([xold])
for i in range(1,N):
xnew=xini+i*dx
ynew=f(xnew) #evaluate_fitness([xnew])
w.create_line(xo+xold*s,yo-yold*s,xo+xnew*s,yo-ynew*s)
# w.create_line(xo+xold*s,yo-yold[0]*s,xo+xnew*s,yo-ynew[0]*s)
xold=xnew
yold=ynew
def graph_population(F,V,mycanvas,escalax,escalay,xcentro,ycentro,color,r):
n_p=len(F)
for I in range(0, n_p):
p=F[I][0]
y=f(p) #evaluate_fitness(p)
mycanvas.create_oval(xcentro+(p-r)*escalax,ycentro-(y-r)*escalay,
xcentro+(p+r)*escalax, ycentro-(y+r)*escalay,
fill=color)
mycanvas.create_line(xcentro+p*escalax,ycentro-y*escalay,
xcentro+(p+V[I][0]*escalax)*escalax,ycentro-y*escalay,fill=color)
#graph_f()
#graph_population(X,V,w,s,s,xo,yo,'blue',0.2)
mainloop()