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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 Multiswarm Particle Swarm Optimization algorithm as
presented in *Blackwell, Branke, and Li, 2008, Particle Swarms for Dynamic
Optimization Problems.*
"""
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)
creator.create("Swarm", 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 convertQuantum(swarm, rcloud, centre, dist):
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))
if dist == "gaussian":
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)]
elif dist == "uvd":
u = random.random()
part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)]
elif dist == "nuvd":
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, creator.Swarm, toolbox.particle)
toolbox.register("update", updateParticle, chi=0.729843788, c=2.05)
toolbox.register("convert", convertQuantum, dist="nuvd")
toolbox.register("evaluate", mpb)
def main(verbose=True):
NSWARMS = 1
NPARTICLES = 5
NEXCESS = 3
RCLOUD = 0.5 # 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"
# Generate the initial population
population = [toolbox.swarm(n=NPARTICLES) for _ in range(NSWARMS)]
# Evaluate each particle
for swarm in population:
for part in swarm:
part.fitness.values = toolbox.evaluate(part)
# Update swarm's attractors personal best and global best
if not part.best or part.fitness > part.bestfit:
part.best = toolbox.clone(part[:]) # Get the position
part.bestfit.values = part.fitness.values # Get the fitness
if not swarm.best or part.fitness > swarm.bestfit:
swarm.best = toolbox.clone(part[:]) # Get the position
swarm.bestfit.values = part.fitness.values # Get the fitness
record = stats.compile(itertools.chain(*population))
logbook.record(gen=0, evals=mpb.nevals, nswarm=len(population),
error=mpb.currentError(), offline_error=mpb.offlineError(), **record)
if verbose:
print(logbook.stream)
generation = 1
while mpb.nevals < 5e5:
# Check for convergence
rexcl = (BOUNDS[1] - BOUNDS[0]) / (2 * len(population)**(1.0/NDIM))
not_converged = 0
worst_swarm_idx = None
worst_swarm = None
for i, swarm in enumerate(population):
# Compute the diameter of the swarm
for p1, p2 in itertools.combinations(swarm, 2):
d = math.sqrt(sum((x1 - x2)**2. for x1, x2 in zip(p1, p2)))
if d > 2*rexcl:
not_converged += 1
# Search for the worst swarm according to its global best
if not worst_swarm or swarm.bestfit < worst_swarm.bestfit:
worst_swarm_idx = i
worst_swarm = swarm
break
# If all swarms have converged, add a swarm
if not_converged == 0:
population.append(toolbox.swarm(n=NPARTICLES))
# If too many swarms are roaming, remove the worst swarm
elif not_converged > NEXCESS:
population.pop(worst_swarm_idx)
# Update and evaluate the swarm
for swarm in population:
# Check for change
if swarm.best and toolbox.evaluate(swarm.best) != swarm.bestfit.values:
# Convert particles to quantum particles
swarm[:] = toolbox.convert(swarm, rcloud=RCLOUD, centre=swarm.best)
swarm.best = None
del swarm.bestfit.values
for part in swarm:
# Not necessary to update if it is a new swarm
# or a swarm just converted to quantum
if swarm.best and part.best:
toolbox.update(part, swarm.best)
part.fitness.values = toolbox.evaluate(part)
# Update swarm's attractors personal best and global best
if not part.best or part.fitness > part.bestfit:
part.best = toolbox.clone(part[:])
part.bestfit.values = part.fitness.values
if not swarm.best or part.fitness > swarm.bestfit:
swarm.best = toolbox.clone(part[:])
swarm.bestfit.values = part.fitness.values
record = stats.compile(itertools.chain(*population))
logbook.record(gen=generation, evals=mpb.nevals, nswarm=len(population),
error=mpb.currentError(), offline_error=mpb.offlineError(), **record)
if verbose:
print(logbook.stream)
# Apply exclusion
reinit_swarms = set()
for s1, s2 in itertools.combinations(range(len(population)), 2):
# Swarms must have a best and not already be set to reinitialize
if population[s1].best and population[s2].best and not (s1 in reinit_swarms or s2 in reinit_swarms):
dist = 0
for x1, x2 in zip(population[s1].best, population[s2].best):
dist += (x1 - x2)**2.
dist = math.sqrt(dist)
if dist < rexcl:
if population[s1].bestfit <= population[s2].bestfit:
reinit_swarms.add(s1)
else:
reinit_swarms.add(s2)
# Reinitialize and evaluate swarms
for s in reinit_swarms:
population[s] = toolbox.swarm(n=NPARTICLES)
for part in population[s]:
part.fitness.values = toolbox.evaluate(part)
# Update swarm's attractors personal best and global best
if not part.best or part.fitness > part.bestfit:
part.best = toolbox.clone(part[:])
part.bestfit.values = part.fitness.values
if not population[s].best or part.fitness > population[s].bestfit:
population[s].best = toolbox.clone(part[:])
population[s].bestfit.values = part.fitness.values
generation += 1
if __name__ == "__main__":
main()
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