def test_custom_works_fine(self):
abc_custom = ArtificialBeeColonyAlgorithm(NP=10,
Limit=2,
seed=self.seed)
abc_customc = ArtificialBeeColonyAlgorithm(NP=10,
Limit=2,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, abc_custom, abc_customc,
MyBenchmark())
def setUp(self):
self.abc_custom = ArtificialBeeColonyAlgorithm(NP=10,
D=40,
nFES=4000,
benchmark=MyBenchmark())
self.abc_griewank = ArtificialBeeColonyAlgorithm(NP=10,
D=40,
nFES=4000,
benchmark='griewank')
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
]
class ABCTestCase(TestCase):
def setUp(self):
self.abc_custom = ArtificialBeeColonyAlgorithm(10, 40, 10000,
MyBenchmark())
self.abc_griewank = ArtificialBeeColonyAlgorithm(
10, 40, 10000, 'griewank')
def test_custom_works_fine(self):
self.assertTrue(self.abc_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.abc_griewank.run())
def test_type_parameters(self): d = ArtificialBeeColonyAlgorithm.typeParameters() self.assertEqual(len(d), 2) self.assertTrue(d['NP'](10)) self.assertFalse(d['NP'](0)) self.assertFalse(d['NP'](-10)) self.assertTrue(d['Limit'](10)) self.assertFalse(d['Limit'](0)) self.assertFalse(d['Limit'](-10))
def __init__(self, algorithm_name, objective, maxfeval, population=30):
super().__init__(algorithm_name, objective, maxfeval)
self.algorithm_name = algorithm_name
self.algo = None #this is the suggest function from hyperopt
if algorithm_name not in __all__:
raise Exception('NiaPy does not have algorithm :' +
str(algorithm_name))
elif self.algorithm_name == 'NiaPyABC':
self.algo = ArtificialBeeColonyAlgorithm(NP=population, Limit=100)
elif self.algorithm_name == 'NiaPyBat':
self.algo = BatAlgorithm(NP=population)
elif self.algorithm_name == 'NiaPyCuckooSearch':
self.algo = CuckooSearch(N=population, pa=0.2, alpha=0.5)
elif self.algorithm_name == 'NiaPyDifferentialEvolution':
self.algo = DifferentialEvolution(NP=population, F=1, CR=0.8)
elif self.algorithm_name == 'NiaPyFireflyAlgorithm':
self.algo = FireflyAlgorithm(NP=population,
alpha=0.5,
betamin=0.2,
gamma=1.0)
elif self.algorithm_name == 'NiaPyGeneticAlgorithm':
self.algo = FireflyAlgorithm(NP=population,
Crossover=UniformCrossover,
Mutation=UniformMutation,
Cr=0.45,
Mr=0.9)
elif self.algorithm_name == 'NiaPyGWO':
self.algo = GreyWolfOptimizer(NP=population)
# config
elif self.algorithm_name == 'NiaPyNelderMead':
self.algo = NelderMeadMethod()
# config
elif self.algorithm_name == 'NiaPyPSO':
self.algo = ParticleSwarmAlgorithm(NP=population,
C1=2,
C2=2,
w=0.9,
vMin=-1.5,
vMax=1.5)
# config
# config
# config
elif self.algorithm_name == 'NiaPySimulatedAnnealing':
self.algo = SimulatedAnnealing(coolingMethod=coolLinear)
# 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 ArtificialBeeColonyAlgorithm
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Artificial Bee Colony Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nFES=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = ArtificialBeeColonyAlgorithm(NP=40, Limit=2)
best = algo.run(task=task)
print('%s -> %s' % (best[0].x, best[1]))
# 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 ArtificialBeeColonyAlgorithm
def Fun(D, sol):
val = 0.0
for i in range(D):
val = val + sol[i] * sol[i]
return val
for i in range(10):
Algorithm = ArtificialBeeColonyAlgorithm(10, 40, 10000, -5.0, 5.0, Fun)
Best = Algorithm.run()
print(Best)
from NiaPy.algorithms.basic import ArtificialBeeColonyAlgorithm
logging.basicConfig()
logger = logging.getLogger('examples')
logger.setLevel('INFO')
# For reproducive results
random.seed(1234)
class MyBenchmark(object):
def __init__(self):
self.Lower = -5
self.Upper = 5
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 = ArtificialBeeColonyAlgorithm(10, 40, 10000, MyBenchmark())
Best = Algorithm.run()
logger.info(Best)
def test_griewank_works_fine(self): abc_griewank = ArtificialBeeColonyAlgorithm(NP=10, D=self.D, nFES=self.nFES, nGEN=self.nGEN, benchmark='griewank', seed=self.seed) abc_griewankc = ArtificialBeeColonyAlgorithm(NP=10, D=self.D, nFES=self.nFES, nGEN=self.nGEN, benchmark='griewank', seed=self.seed) AlgorithmTestCase.algorithm_run_test(self, abc_griewank, abc_griewankc)
def test_custom_works_fine(self): abc_custom = ArtificialBeeColonyAlgorithm(NP=10, D=self.D, nFES=self.nFES, nGEN=self.nGEN, Limit=2, benchmark=MyBenchmark(), seed=self.seed) abc_customc = ArtificialBeeColonyAlgorithm(NP=10, D=self.D, nFES=self.nFES, nGEN=self.nGEN, Limit=2, benchmark=MyBenchmark(), seed=self.seed) AlgorithmTestCase.algorithm_run_test(self, abc_custom, abc_customc)
logger.setLevel('INFO')
# For reproducive results
random.seed(1234)
class MyBenchmark(object):
def __init__(self):
self.Lower = -5
self.Upper = 5
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 = ArtificialBeeColonyAlgorithm(NP=10,
D=40,
nFES=10000,
benchmark=MyBenchmark())
Best = Algorithm.run()
logger.info(Best)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
def test_griewank_works_fine(self):
abc_griewank = ArtificialBeeColonyAlgorithm(NP=10, seed=self.seed)
abc_griewankc = ArtificialBeeColonyAlgorithm(NP=10, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, abc_griewank, abc_griewankc)
def setUp(self):
self.abc_custom = ArtificialBeeColonyAlgorithm(10, 40, 10000,
MyBenchmark())
self.abc_griewank = ArtificialBeeColonyAlgorithm(
10, 40, 10000, 'griewank')
