Particle swarm optimization: Difference between revisions
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f(2.20291, 1.57080) : -1.801303
</pre>
=={{header|Python}}==
{{trans|D}}
<lang python>import math
import random
INFINITY = 1 << 127
MAX_INT = 1 << 31
class Parameters:
def __init__(self, omega, phip, phig):
self.omega = omega
self.phip = phip
self.phig = phig
class State:
def __init__(self, iter, gbpos, gbval, min, max, parameters, pos, vel, bpos, bval, nParticles, nDims):
self.iter = iter
self.gbpos = gbpos
self.gbval = gbval
self.min = min
self.max = max
self.parameters = parameters
self.pos = pos
self.vel = vel
self.bpos = bpos
self.bval = bval
self.nParticles = nParticles
self.nDims = nDims
def report(self, testfunc):
print "Test Function :", testfunc
print "Iterations :", self.iter
print "Global Best Position :", self.gbpos
print "Global Best Value : %.16f" % self.gbval
def uniform01():
v = random.random()
assert 0.0 <= v and v < 1.0
return v
def psoInit(min, max, parameters, nParticles):
nDims = len(min)
pos = [min[:]] * nParticles
vel = [[0.0] * nDims] * nParticles
bpos = [min[:]] * nParticles
bval = [INFINITY] * nParticles
iter = 0
gbpos = [INFINITY] * nDims
gbval = INFINITY
return State(iter, gbpos, gbval, min, max, parameters, pos, vel, bpos, bval, nParticles, nDims);
def pso(fn, y):
p = y.parameters
v = [0.0] * (y.nParticles)
bpos = [y.min[:]] * (y.nParticles)
bval = [0.0] * (y.nParticles)
gbpos = [0.0] * (y.nDims)
gbval = INFINITY
for j in xrange(0, y.nParticles):
# evaluate
v[j] = fn(y.pos[j])
# update
if v[j] < y.bval[j]:
bpos[j] = y.pos[j][:]
bval[j] = v[j]
else:
bpos[j] = y.bpos[j][:]
bval[j] = y.bval[j]
if bval[j] < gbval:
gbval = bval[j]
gbpos = bpos[j][:]
rg = uniform01()
pos = [[None] * (y.nDims)] * (y.nParticles)
vel = [[None] * (y.nDims)] * (y.nParticles)
for j in xrange(0, y.nParticles):
# migrate
rp = uniform01()
ok = True
vel[j] = [0.0] * (len(vel[j]))
pos[j] = [0.0] * (len(pos[j]))
for k in xrange(0, y.nDims):
vel[j][k] = p.omega * y.vel[j][k] \
+ p.phip * rp * (bpos[j][k] - y.pos[j][k]) \
+ p.phig * rg * (gbpos[k] - y.pos[j][k])
pos[j][k] = y.pos[j][k] + vel[j][k]
ok = ok and y.min[k] < pos[j][k] and y.max[k] > pos[j][k]
if not ok:
for k in xrange(0, y.nDims):
pos[j][k] = y.min[k] + (y.max[k] - y.min[k]) * uniform01()
iter = 1 + y.iter
return State(iter, gbpos, gbval, y.min, y.max, y.parameters, pos, vel, bpos, bval, y.nParticles, y.nDims);
def iterate(fn, n, y):
r = y
old = y
if n == MAX_INT:
while True:
r = pso(fn, r)
if r == old:
break
old = r
else:
for _ in xrange(0, n):
r = pso(fn, r)
return r
def mccormick(x):
(a, b) = x
return math.sin(a + b) + (a - b) * (a - b) + 1.0 + 2.5 * b - 1.5 * a
def michalewicz(x):
m = 10
d = len(x)
sum = 0.0
for i in xrange(1, d):
j = x[i - 1]
k = math.sin(i * j * j / math.pi)
sum += math.sin(j) * k ** (2.0 * m)
return -sum
def main():
state = psoInit([-1.5, -3.0], [4.0, 4.0], Parameters(0.0, 0.6, 0.3), 100)
state = iterate(mccormick, 40, state)
state.report("McCormick")
print "f(-.54719, -1.54719) : %.16f" % (mccormick([-.54719, -1.54719]))
print
state = psoInit([0.0, 0.0], [math.pi, math.pi], Parameters(0.3, 0.3, 0.3), 1000)
state = iterate(michalewicz, 30, state)
state.report("Michalewicz (2D)")
print "f(2.20, 1.57) : %.16f" % (michalewicz([2.2, 1.57]))
main()</lang>
{{out}}
<pre>Test Function : McCormick
Iterations : 40
Global Best Position : [-0.5471069930124911, -1.5471582891466962]
Global Best Value : -1.9132229450518705
f(-.54719, -1.54719) : -1.9132229548822739
Test Function : Michalewicz (2D)
Iterations : 30
Global Best Position : [2.2029052187108036, 0.9404640520657541]
Global Best Value : -0.8013034100970750
f(2.20, 1.57) : -0.8011663878202856</pre>
=={{header|Racket}}==
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