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# This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
"""Implementation of the Speciation Particle Swarm Optimization algorithm as
presented in *Li, Blackwell, and Branke, 2006, Particle Swarm with Speciation
and Adaptation in a Dynamic Environment.*
"""
import itertools
import math
import operator
import random
import numpy
try:
from itertools import imap
except:
# Python 3 nothing to do
pass
else:
map = imap
from deap import base
from deap.benchmarks import movingpeaks
from deap import creator
from deap import tools
scenario = movingpeaks.SCENARIO_2
NDIM = 5
BOUNDS = [scenario["min_coord"], scenario["max_coord"]]
mpb = movingpeaks.MovingPeaks(dim=NDIM, **scenario)
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Particle", list, fitness=creator.FitnessMax, speed=list,
best=None, bestfit=creator.FitnessMax)
def generate(pclass, dim, pmin, pmax, smin, smax):
part = pclass(random.uniform(pmin, pmax) for _ in range(dim))
part.speed = [random.uniform(smin, smax) for _ in range(dim)]
return part
def convert_quantum(swarm, rcloud, centre):
dim = len(swarm[0])
for part in swarm:
position = [random.gauss(0, 1) for _ in range(dim)]
dist = math.sqrt(sum(x**2 for x in position))
# Gaussian distribution
# u = abs(random.gauss(0, 1.0/3.0))
# part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)]
# UVD distribution
# u = random.random()
# part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)]
# NUVD distribution
u = abs(random.gauss(0, 1.0/3.0))
part[:] = [(rcloud * x * u / dist) + c for x, c in zip(position, centre)]
del part.fitness.values
del part.bestfit.values
part.best = None
return swarm
def updateParticle(part, best, chi, c):
ce1 = (c*random.uniform(0, 1) for _ in range(len(part)))
ce2 = (c*random.uniform(0, 1) for _ in range(len(part)))
ce1_p = map(operator.mul, ce1, map(operator.sub, best, part))
ce2_g = map(operator.mul, ce2, map(operator.sub, part.best, part))
a = map(operator.sub,
map(operator.mul,
itertools.repeat(chi),
map(operator.add, ce1_p, ce2_g)),
map(operator.mul,
itertools.repeat(1-chi),
part.speed))
part.speed = list(map(operator.add, part.speed, a))
part[:] = list(map(operator.add, part, part.speed))
toolbox = base.Toolbox()
toolbox.register("particle", generate, creator.Particle, dim=NDIM,
pmin=BOUNDS[0], pmax=BOUNDS[1], smin=-(BOUNDS[1] - BOUNDS[0])/2.0,
smax=(BOUNDS[1] - BOUNDS[0])/2.0)
toolbox.register("swarm", tools.initRepeat, list, toolbox.particle)
toolbox.register("update", updateParticle, chi=0.729843788, c=2.05)
toolbox.register("convert", convert_quantum)
toolbox.register("evaluate", mpb)
def main(verbose=True):
NPARTICLES = 100
RS = (BOUNDS[1] - BOUNDS[0]) / (50**(1.0/NDIM)) # between 1/20 and 1/10 of the domain's range
PMAX = 10
RCLOUD = 1.0 # 0.5 times the move severity
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = "gen", "nswarm", "evals", "error", "offline_error", "avg", "max"
swarm = toolbox.swarm(n=NPARTICLES)
generation = 0
while mpb.nevals < 5e5:
# Evaluate each particle in the swarm
for part in swarm:
part.fitness.values = toolbox.evaluate(part)
if not part.best or part.bestfit < part.fitness:
part.best = toolbox.clone(part[:]) # Get the position
part.bestfit.values = part.fitness.values # Get the fitness
# Sort swarm into species, best individual comes first
sorted_swarm = sorted(swarm, key=lambda ind: ind.bestfit, reverse=True)
species = []
while sorted_swarm:
found = False
for s in species:
dist = math.sqrt(sum((x1 - x2)**2 for x1, x2 in zip(sorted_swarm[0].best, s[0].best)))
if dist <= RS:
found = True
s.append(sorted_swarm[0])
break
if not found:
species.append([sorted_swarm[0]])
sorted_swarm.pop(0)
record = stats.compile(swarm)
logbook.record(gen=generation, evals=mpb.nevals, nswarm=len(species),
error=mpb.currentError(), offline_error=mpb.offlineError(), **record)
if verbose:
print(logbook.stream)
# Detect change
if any(s[0].bestfit.values != toolbox.evaluate(s[0].best) for s in species):
# Convert particles to quantum particles
for s in species:
s[:] = toolbox.convert(s, rcloud=RCLOUD, centre=s[0].best)
else:
# Replace exceeding particles in a species with new particles
for s in species:
if len(s) > PMAX:
n = len(s) - PMAX
del s[PMAX:]
s.extend(toolbox.swarm(n=n))
# Update particles that have not been reinitialized
for s in species[:-1]:
for part in s[:PMAX]:
toolbox.update(part, s[0].best)
del part.fitness.values
# Return all but the worst species' updated particles to the swarm
# The worst species is replaced by new particles
swarm = list(itertools.chain(toolbox.swarm(n=len(species[-1])), *species[:-1]))
generation += 1
if __name__ == '__main__':
main()
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