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(10010, evals)
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_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_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 RunAlgorithm(self, NUM_RUNS,D,Np,nFES,BenchFunction):
rawData = np.zeros(NUM_RUNS)
for i in range(NUM_RUNS):
task = StoppingTask(D=D, nFES=nFES, optType=OptimizationType.MINIMIZATION, benchmark=BenchFunction)
algo = FireflyAlgorithm(NP=Np, alpha=0.5, betamin=0.2, gamma=1.0)
best = algo.run(task=task)
rawData[i] = best[1]
print(rawData[i])
return rawData
def setUp(self):
self.D = 20
rng = default_rng()
self.x, self.task = default_rng().uniform(
-100, 100, self.D), StoppingTask(D=self.D,
nFES=230,
nGEN=np.inf,
benchmark=MyBenchmark())
self.s1, self.s2, self.s3 = Individual(x=self.x, e=False), Individual(
task=self.task, rng=rng), Individual(task=self.task)
def executeGA(typeBenchmark):
task = StoppingTask(
D=dimensoes,
nFES=numeroAvaliacoes,
optType=tipoOtimizacao,
logger=True,
benchmark=typeBenchmark,
)
algo = GeneticAlgorithm(
NP=tamanhoPopulacao,
Crossover=crossover,
Mutation=mutation,
Cr=taxaCruzamento,
Mr=taxaMutacao,
)
best = algo.run(task=task)
return [task, best[1], best[0]]
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]]
def eval(self, A):
r"""Check if is algorithm trying to evaluate solution out of bounds."""
self.assertTrue(self.isFeasible(A),
'Solution %s is not in feasible space!!!' % A)
return StoppingTask.eval(self, A)
# 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.task import StoppingTask, OptimizationType
from NiaPy.algorithms.basic import BeesAlgorithm
import sys
sys.path.append('../')
# End of fix
# we will run Bees Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=20,
nGEN=2,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = BeesAlgorithm(NP=50, m=20, e=10, nep=20, nsp=15, ngh=7)
best = algo.run(task=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.basic import ParticleSwarmAlgorithm
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Benchmark
class MyBenchmark(Benchmark):
def __init__(self):
Benchmark.__init__(self, -10, 10)
def function(self):
def evaluate(D, sol):
val = 0.0
for i in range(D): val += sol[i] ** 2
return val
return evaluate
# we will run Particle Swarm Algorithm with on custom benchmark
for i in range(1):
task = StoppingTask(D=10, nGEN=1000, optType=OptimizationType.MINIMIZATION, benchmark=MyBenchmark())
algo = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4)
best = algo.run(task=task)
print('%s -> %s ' % (best[0], 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
import logging
from NiaPy.task.task import StoppingTask, OptimizationType
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,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
best = algo.run(task)
print('%s -> %s' % (best[0].x, best[1]))
print('---------------------------------------')
# 2 Number of generations (iterations) as a stopping criteria
for i in range(10):
task = StoppingTask(D=10,
nGEN=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
best = algo.run(task)
# 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.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run ParticleSwarmAlgorithm for 1 independent runs
for i in range(1):
task = StoppingTask(D=10, nFES=1000, optType=OptimizationType.MINIMIZATION, logger=True, 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('%s -> %s' % (best[0], best[1]))
#plot a convergence graph
task.plot()
NUM_RUNS = 25
startWholeAlgoTime = time.time()
with pd.ExcelWriter('results.xlsx') as writer:
for Np in ArrayOfNP:
for nFES in ArrayOfnFES:
for D in ArrayOfD:
tempDataFrame = []
for BenchFunction in ArrayOfBenchmarks:
resultArray = []
rawData = np.zeros(NUM_RUNS)
start = time.time()
for i in range(NUM_RUNS):
task = StoppingTask(
D=D,
nFES=nFES,
optType=OptimizationType.MINIMIZATION,
benchmark=BenchFunction)
algo = FireflyAlgorithm(NP=Np,
alpha=0.5,
betamin=0.2,
gamma=1.0)
best = algo.run(task=task)
rawData[i] = best[1]
end = time.time()
elapsedTime = end - start
processedData = BasicStatistics(rawData)
#statistics
resultArray.append(processedData.min_value())
resultArray.append(processedData.max_value())
resultArray.append(processedData.mean())
# 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.task import StoppingTask, OptimizationType
from NiaPy.algorithms.basic import DifferentialEvolution
from NiaPy.benchmarks import Sphere
# Storing improvements during the evolutionary cycle
for i in range(1):
task = StoppingTask(D=10,
nFES=10000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = DifferentialEvolution(NP=40, CR=0.9, F=0.5)
best = algo.run(task)
evals, x_f = task.return_conv()
print(evals) # print function evaluations
print(x_f) # print values
def setUp(self):
self.D = 20
self.x, self.task = rnd.uniform(-100, 100, self.D), StoppingTask(
D=self.D, nFES=230, nGEN=inf, benchmark=MyBenchmark())
self.s1, self.s2, self.s3 = Individual(x=self.x, e=False), Individual(
task=self.task, rand=rnd), Individual(task=self.task)
# 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 MultiStrategyDifferentialEvolutionMTSv1
from NiaPy.algorithms.basic.de import CrossCurr2Best1, CrossBest1
from NiaPy.task.task import StoppingTask, OptimizationType
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,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = MultiStrategyDifferentialEvolutionMTSv1(
NP=70, F=0.5, CR=0.4, strategies=(CrossBest1, CrossCurr2Best1))
best = algo.run(task=task)
print('%s -> %s' % (best[0].x, 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
from NiaPy.algorithms.basic import BareBonesFireworksAlgorithm
from NiaPy.task.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, nFES=1000, benchmark=Sphere())
algo = BareBonesFireworksAlgorithm()
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
