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pso_basic.py
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pso_basic.py
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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/>.
import operator
import random
from deap import base
from deap import benchmarks
from deap import creator
from deap import tools
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Particle", list, fitness=creator.FitnessMax, speed=list,
smin=None, smax=None, best=None)
def generate(size, pmin, pmax, smin, smax):
part = creator.Particle(random.uniform(pmin, pmax) for _ in range(size))
part.speed = [random.uniform(smin, smax) for _ in range(size)]
part.smin = smin
part.smax = smax
return part
def updateParticle(part, best, phi1, phi2):
u1 = (random.uniform(0, phi1) for _ in range(len(part)))
u2 = (random.uniform(0, phi2) for _ in range(len(part)))
v_u1 = map(operator.mul, u1, map(operator.sub, part.best, part))
v_u2 = map(operator.mul, u2, map(operator.sub, best, part))
part.speed = list(map(operator.add, part.speed, map(operator.add, v_u1, v_u2)))
for i, speed in enumerate(part.speed):
if speed < part.smin:
part.speed[i] = part.smin
elif speed > part.smax:
part.speed[i] = part.smax
part[:] = list(map(operator.add, part, part.speed))
toolbox = base.Toolbox()
toolbox.register("particle", generate, size=2, pmin=-6, pmax=6, smin=-3, smax=3)
toolbox.register("population", tools.initRepeat, list, toolbox.particle)
toolbox.register("update", updateParticle, phi1=2.0, phi2=2.0)
toolbox.register("evaluate", benchmarks.h1)
def main():
pop = toolbox.population(n=5)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("Avg", tools.mean)
stats.register("Std", tools.std)
stats.register("Min", min)
stats.register("Max", max)
column_names = ["gen", "evals"]
column_names.extend(stats.functions.keys())
logger = tools.EvolutionLogger(column_names)
logger.logHeader()
GEN = 1000
best = None
for g in range(GEN):
for part in pop:
part.fitness.values = toolbox.evaluate(part)
if not part.best or part.best.fitness < part.fitness:
part.best = creator.Particle(part)
part.best.fitness.values = part.fitness.values
if not best or best.fitness < part.fitness:
best = creator.Particle(part)
best.fitness.values = part.fitness.values
for part in pop:
toolbox.update(part, best)
# Gather all the fitnesses in one list and print the stats
stats.update(pop)
logger.logGeneration(gen=g, evals=len(pop), stats=stats)
return pop, stats, best
if __name__ == "__main__":
main()