class CSTestCase(TestCase):
def setUp(self):
self.pso_custom = ParticleSwarmAlgorithm(NP=40,
D=40,
nFES=1000,
C1=2.0,
C2=2.0,
w=0.7,
vMin=-4,
vMax=4,
benchmark=MyBenchmark())
self.pso_griewank = ParticleSwarmAlgorithm(NP=40,
D=40,
nFES=1000,
C1=2.0,
C2=2.0,
w=0.7,
vMin=-4,
vMax=4,
benchmark='griewank')
def test_custom_works_fine(self):
self.assertTrue(self.pso_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.pso_griewank.run())
class CSTestCase(TestCase):
def setUp(self):
self.pso_custom = ParticleSwarmAlgorithm(40, 40, 1000, 2.0, 2.0, 0.7,
-4, 4, MyBenchmark())
self.pso_griewank = ParticleSwarmAlgorithm(40, 40, 1000, 2.0, 2.0, 0.7,
-4, 4, 'griewank')
def test_custom_works_fine(self):
self.assertTrue(self.pso_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.pso_griewank.run())
def optimize(bench, algo):
average_mfo = 0
average_de = 0
average_abc = 0
average_pso = 0
average_ba = 0
average_fa = 0
average_ga = 0
for i in np.arange(epoch):
mfo = MothFlameOptimizer(D=dim, NP=pop, nGEN=maxIter, benchmark=bench)
de = DifferentialEvolution(D=dim,
NP=pop,
nGEN=maxIter,
benchmark=bench)
abc = ArtificialBeeColonyAlgorithm(D=dim,
NP=pop,
nFES=maxIter,
benchmark=bench)
pso = ParticleSwarmAlgorithm(D=dim,
NP=pop,
nGEN=maxIter,
benchmark=bench)
ba = BatAlgorithm(D=dim, NP=pop, nFES=maxIter, benchmark=bench)
fa = FireflyAlgorithm(D=dim, NP=pop, nFES=maxIter, benchmark=bench)
ga = GeneticAlgorithm(D=dim, NP=pop, nFES=maxIter, benchmark=bench)
gen, best_de = de.run()
gen, best_mfo = mfo.run()
gen, best_abc = abc.run()
gen, best_pso = pso.run()
gen, best_ba = ba.run()
gen, best_fa = fa.run()
gen, best_ga = ga.run()
average_mfo += best_de / epoch
average_de += best_mfo / epoch
average_abc += best_abc / epoch
average_pso += best_pso / epoch
average_ba += best_ba / epoch
average_fa += best_fa / epoch
average_ga += best_ga / epoch
print(algo, ': DE Average of Bests over', epoch, 'run: ', average_de)
print(algo, ': MFO Average of Bests over', epoch, 'run: ', average_mfo)
print(algo, ': ABC Average of Bests over', epoch, 'run: ', average_abc)
print(algo, ': PSO Average of Bests over', epoch, 'run: ', average_pso)
print(algo, ': BA Average of Bests over', epoch, 'run: ', average_ba)
print(algo, ': FA Average of Bests over', epoch, 'run: ', average_fa)
print(algo, ': GA Average of Bests over', epoch, 'run: ', average_ga)
return [
average_de, average_mfo, average_abc, average_pso, average_ba,
average_fa, average_ga
]
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = ParticleSwarmAlgorithm(NP=50,
C1=2.0,
C2=2.0,
w=0.5,
vMin=-5,
vMax=5,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def logging_example():
task = TaskConvPrint(D=50, nFES=50000, nGEN=50000, benchmark=MyBenchmark())
algo = ParticleSwarmAlgorithm(NP=50,
C1=2.0,
C2=2.0,
w=0.5,
vMin=-5,
vMax=5,
seed=None,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def simple_example(runs=10):
for i in range(10):
algo = ParticleSwarmAlgorithm(NP=50,
D=40,
nFES=40000,
C1=2.0,
C2=2.0,
w=0.5,
vMin=-5,
vMax=5,
seed=i,
benchmark=MyBenchmark())
Best = algo.run()
logger.info('%s %s' % (Best[0], Best[1]))
def executePSO(typeBenchmark):
task = StoppingTask(
D=dimensoes,
nFES=numeroAvaliacoes,
optType=tipoOtimizacao,
benchmark=typeBenchmark,
)
algo = ParticleSwarmAlgorithm(
NP=tamanhoPopulacao,
C1=componenteCognitivo,
C2=componenteSocial,
w=pesoInercial,
vMin=velocidadeMinima,
vMax=velocidadeMaxima,
)
best = algo.run(task=task)
return [task, best[1], best[0]]
# encoding=utf8
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
import sys
sys.path.append('../')
# End of fix
import random
from NiaPy.algorithms.basic import ParticleSwarmAlgorithm
from NiaPy.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
#we will run ParticleSwarmAlgorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nFES=1000, optType=OptimizationType.MAXIMIZATION, benchmark=Sphere())
algo = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4)
best = algo.run(task=task)
print best
logging.basicConfig()
logger = logging.getLogger('examples')
logger.setLevel('INFO')
# For reproducive results
random.seed(1234)
class MyBenchmark(object):
def __init__(self):
self.Lower = -11
self.Upper = 11
def function(self):
def evaluate(D, sol):
val = 0.0
for i in range(D):
val = val + sol[i] * sol[i]
return val
return evaluate
for i in range(10):
Algorithm = ParticleSwarmAlgorithm(50, 40, 40000, 2.0, 2.0, 0.5, -5, 5,
MyBenchmark())
Best = Algorithm.run()
logger.info(Best)
