def test_griewank_works_fine(self):
mke_griewank = MonkeyKingEvolutionV1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
mke_griewankc = MonkeyKingEvolutionV1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, mke_griewank, mke_griewankc)
def test_griewank_works_fine(self):
fwa_griewank = FireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
fwa_griewankc = FireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_griewank, fwa_griewankc)
def setUp(self):
self.D = 40
self.mkev2_custom = MonkeyKingEvolutionV2(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.mkev2_griewank = MonkeyKingEvolutionV2(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def test_griewank_works_fine(self):
fun = Griewank().function()
x = self.mkev2_griewank.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.D, x[0]), x[1], delta=1e2)
r=0.5,
Qmin=0.0,
Qmax=2.0,
benchmark=MyBenchmark())
Best = Algorithm.run()
logger.info(Best)
# example using predifined benchmark function
# available benchmarks are:
# - griewank
# - rastrigin
# - rosenbrock
# - sphere
logger.info('Running with default Griewank benchmark...')
griewank = Griewank()
for i in range(10):
Algorithm = BatAlgorithm(D=10,
NP=40,
nFES=10000,
A=0.5,
r=0.5,
Qmin=0.0,
Qmax=2.0,
benchmark=griewank)
Best = Algorithm.run()
Best = Algorithm.run()
logger.info(Best)
logger.info(
# 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
from NiaPy.algorithms.modified import HybridSelfAdaptiveBatAlgorithm
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Griewank
# we will run Bat Algorithm for 5 independent runs
algo = HybridSelfAdaptiveBatAlgorithm(NP=50)
for i in range(5):
task = StoppingTask(D=10,
nGEN=10000,
benchmark=Griewank(Upper=600, Lower=-600))
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print(algo.getParameters())
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
Ackley,
Griewank,
Sphere,
HappyCat
)
"""Example demonstrating the use of NiaPy Runner."""
runner = Runner(
D=40,
nFES=100,
nRuns=2,
useAlgorithms=[
GreyWolfOptimizer(),
"FlowerPollinationAlgorithm",
ParticleSwarmAlgorithm(),
"HybridBatAlgorithm",
"SimulatedAnnealing",
"CuckooSearch"],
useBenchmarks=[
Ackley(),
Griewank(),
Sphere(),
HappyCat(),
"rastrigin"]
)
print(runner.run(verbose=True))
# 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
from NiaPy.algorithms.modified import SelfAdaptiveDifferentialEvolution
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Griewank
# we will run jDE algorithm for 5 independent runs
algo = SelfAdaptiveDifferentialEvolution(NP=40, F=0.5, F_l=0.0, F_u=2.0, Tao1=0.9, CR=0.5, Tao2=0.45)
for i in range(5):
task = StoppingTask(D=10, nFES=10000, benchmark=Griewank(Lower=-600, Upper=600), logger=True)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print(algo.getParameters())
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
# 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
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Griewank
from NiaPy.algorithms.basic import DifferentialEvolution
# we will run Differential Evolution for 5 independent runs
algo = DifferentialEvolution(NP=50, F=0.5, CR=0.9)
for i in range(5):
task = StoppingTask(D=10,
nFES=10000,
benchmark=Griewank(Lower=-600, Upper=600),
logger=True)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print(algo.getParameters())
# 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
from NiaPy.algorithms.modified import AdaptiveBatAlgorithm
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Griewank
# we will run Bat Algorithm for 5 independent runs
algo = AdaptiveBatAlgorithm()
for i in range(5):
task = StoppingTask(D=10, nGEN=10000, benchmark=Griewank(Lower=-600, Upper=600))
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print(algo.getParameters())
# 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
from NiaPy.algorithms.basic import AntColonyOptimization
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
from NiaPy.benchmarks import Ackley
from NiaPy.benchmarks import Griewank
# we will run Ant Colony Optimization for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nGEN=1000, optType=OptimizationType.MINIMIZATION, benchmark=Griewank())
algo = AntColonyOptimization(NP=40)
best = algo.run(task=task)
print('%s -> %s' % (best[0], best[1]))