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"""Particle Swarm Optimization
:Author: Robert Kern
Copyright 2005 by Robert Kern.
"""
import scipy as sp
def pso(func, nswarm, lbound, ubound, vmax, args=(), maxiter=1000, cp=2.0, cg=2.0):
ndim = len(lbound)
lbound = sp.asarray(lbound)
ubound = sp.asarray(ubound)
vmax = sp.asarray(vmax)
# initialize the swarm
swarm = lbound + sp.rand(nswarm, ndim)*(ubound-lbound)
# initialize the "personal best" values
pbestv = sp.zeros(nswarm, sp.Float)
for i in sp.arange(nswarm):
pbestv[i] = func(swarm[i])
pbest = sp.array(swarm)
# initialize the "global best" values
gbesti = sp.argmin(pbestv)
gbestv = pbestv[gbesti]
gbest = pbest[gbesti]
# initialize velocities
velocities = 2*vmax*sp.randn(nswarm, ndim) - vmax
for i in sp.arange(maxiter):
values = sp.zeros(nswarm, sp.Float)
for j in sp.arange(nswarm):
values[j] = func(swarm[j])
mask = values < pbestv
mask2d = sp.repeat(mask, ndim)
mask2d.shape = (nswarm, ndim)
pbestv = sp.where(mask, values, pbestv)
pbest = sp.where(mask2d, swarm, pbest)
if sp.minimum.reduce(pbestv) < gbestv:
gbesti = sp.argmin(pbestv)
gbestv = pbestv[gbesti]
gbest = pbest[gbesti]
velocities += (cp*sp.rand()*(pbest - swarm) +
cg*sp.rand()*(gbest - swarm))
velocities = sp.clip(velocities, -vmax, vmax)
swarm += velocities
swarm = sp.clip(swarm, lbound, ubound)
yield gbest
## return gbest
def _testfunc(x):
return -sp.multiply.reduce(sp.cos(x/2.0/sp.pi))*sp.exp(-sp.sum(x*x))
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