def test_FA_iters_fine(self):
task = StoppingTask(D=10,
nGEN=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = FireflyAlgorithm(NP=25)
algo.runTask(task)
iters = task.iters()
self.assertEqual(1000, iters)
def test_BA_iters_to_fes(self):
task = StoppingTask(D=10,
nGEN=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = BatAlgorithm(NP=10)
algo.runTask(task)
evals = task.evals()
self.assertEqual(10000, evals)
def test_FA_evals_fine(self):
task = StoppingTask(D=10,
nFES=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = FireflyAlgorithm(NP=25)
algo.runTask(task)
evals = task.evals()
self.assertEqual(1000, evals)
def test_BA_iters_to_fes(self):
task = Task(D=10,
nGEN=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = BatAlgorithm(task=task, NP=10)
algo.run()
evals = algo.task.evals()
self.assertEqual(evals, 10010)
def test_DE_iters_fine(self):
task = StoppingTask(D=10,
nGEN=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
algo.runTask(task)
iters = task.iters()
self.assertEqual(1000, iters)
def test_FA_evals_fine(self):
task = Task(D=10,
nFES=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = FireflyAlgorithm(task=task, NP=25)
algo.run()
evals = algo.task.evals()
self.assertEqual(evals, 1000)
def test_FA_iters_fine(self):
task = Task(D=10,
nGEN=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = FireflyAlgorithm(task=task, NP=25)
algo.run()
iters = algo.task.iters()
self.assertEqual(iters, 1000)
def test_DE_evals_fine(self):
task = Task(D=10,
nFES=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = DifferentialEvolution(task=task, NP=40, CR=0.9, F=0.5)
algo.run()
evals = algo.task.evals()
self.assertEqual(evals, 1000)
def function(self):
return Sphere(self.Lower, self.Upper).function()
# encoding=utf8
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
from NiaPy.benchmarks import Sphere
from NiaPy.task import StoppingTask
from NiaPy.algorithms.basic import BeesAlgorithm
import sys
sys.path.append('../')
# we will run Bees Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=20, nGEN=2, benchmark=Sphere())
algo = BeesAlgorithm(NP=50, m=20, e=10, nep=20, nsp=15, ngh=7)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
# 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 CoralReefsOptimization
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
#we will run Coral Reefs Optimization algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nFES=1000, optType=OptimizationType.MINIMIZATION, benchmark=Sphere())
algo = CoralReefsOptimization(NP=40)
best = algo.run(task=task)
print best
# encoding=utf8
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
from NiaPy.algorithms.basic import CovarianceMatrixAdaptionEvolutionStrategy
from NiaPy.benchmarks import Sphere
from NiaPy.task import StoppingTask
import sys
sys.path.append('../')
# End of fix
# we will run CMA-ES for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nFES=1000, logger=True, benchmark=Sphere())
algo = CovarianceMatrixAdaptionEvolutionStrategy(NP=20)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
# 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 ParameterFreeBatAlgorithm
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Sphere
algo = ParameterFreeBatAlgorithm()
for i in range(10):
task = StoppingTask(D=10, nFES=10000, benchmark=Sphere(Upper=5.12, Lower=-5.12))
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
import random
import logging
from NiaPy.task import StoppingTask
from NiaPy.algorithms.basic import DifferentialEvolution
from NiaPy.benchmarks import Griewank, Sphere
# 1 Number of function evaluations (nFES) as a stopping criteria
for i in range(10):
task = StoppingTask(D=10, nFES=10000, benchmark=Sphere())
algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print('---------------------------------------')
# 2 Number of generations (iterations) as a stopping criteria
for i in range(10):
task = StoppingTask(D=10, nGEN=1000, benchmark=Sphere())
algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print('---------------------------------------')
# 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 ComprehensiveLearningParticleSwarmOptimizer
from NiaPy.benchmarks import Sphere
from NiaPy.task import StoppingTask
# we will run ParticleSwarmAlgorithm for 5 independent runs
algo = ComprehensiveLearningParticleSwarmOptimizer(NP=50,
C1=.3,
C2=1.0,
m=5,
w=0.86,
vMin=-2,
vMax=2)
for i in range(5):
task = StoppingTask(D=25, nFES=20000, benchmark=Sphere())
best = algo.run(task=task)
print('%s -> %f' % (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.algorithms.basic import KrillHerdV2
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Sphere
# we will run Fireworks Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nGEN=50, benchmark=Sphere())
algo = KrillHerdV2(NP=70, Ainit=0.1, Afinal=0.9)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
# 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
import random
from NiaPy.algorithms.basic import AgingNpMultiMutationDifferentialEvolution
from NiaPy.algorithms.basic.de import CrossCurr2Best1, CrossBest2
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Sphere
# we will run Differential Evolution for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nFES=5000, benchmark=Sphere())
algo = AgingNpMultiMutationDifferentialEvolution(NP=10, F=0.2, CR=0.65, strategies=(CrossCurr2Best1, CrossBest2), delta_np=0.05, omega=0.9)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
# 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.basic import GravitationalSearchAlgorithm
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Gravitational Search Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nFES=10000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = GravitationalSearchAlgorithm(NP=40)
best = algo.run(task=task)
print(best)
# 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 Sphere
from NiaPy.algorithms.other import RandomSearch
for i in range(1):
task = StoppingTask(D=5, nGEN=5000, benchmark=Sphere())
algo = RandomSearch()
best = algo.run(task=task)
print(best)