def test_custom1_works_fine(self):
ca_custom = SimulatedAnnealing(seed=self.seed,
coolingMethod=coolLinear)
ca_customc = SimulatedAnnealing(seed=self.seed,
coolingMethod=coolLinear)
AlgorithmTestCase.algorithm_run_test(self, ca_custom, ca_customc,
MyBenchmark())
def test_griewank1_works_fine(self):
ca_griewank = SimulatedAnnealing(NP=40,
seed=self.seed,
coolingMethod=coolLinear)
ca_griewankc = SimulatedAnnealing(NP=40,
seed=self.seed,
coolingMethod=coolLinear)
AlgorithmTestCase.algorithm_run_test(self, ca_griewank, ca_griewankc)
def test_griewank_works_fine(self):
ca_griewank = SimulatedAnnealing(NP=40,
D=self.D,
nGEN=self.nGEN,
nFES=self.nFES,
benchmark=Griewank(),
seed=self.seed)
ca_griewankc = SimulatedAnnealing(NP=40,
D=self.D,
nGEN=self.nGEN,
nFES=self.nFES,
benchmark=Griewank(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ca_griewank, ca_griewankc)
def test_custom_works_fine(self):
ca_custom = SimulatedAnnealing(NP=40,
D=self.D,
nGEN=self.nGEN,
nFES=self.nFES,
benchmark=MyBenchmark(),
seed=self.seed)
ca_customc = SimulatedAnnealing(NP=40,
D=self.D,
nGEN=self.nGEN,
nFES=self.nFES,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ca_custom, ca_customc)
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.other import SimulatedAnnealing
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
from NiaPy.algorithms.other.sa import coolLinear
# we will run Simulated Annealing for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nGEN=10000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = SimulatedAnnealing(coolingMethod=coolLinear)
best = algo.run(task=task)
print(best)
def test_griewank_works_fine(self):
ca_griewank = SimulatedAnnealing(NP=40, seed=self.seed)
ca_griewankc = SimulatedAnnealing(NP=40, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ca_griewank, ca_griewankc)
def test_custom_works_fine(self):
ca_custom = SimulatedAnnealing(NP=40, seed=self.seed)
ca_customc = SimulatedAnnealing(NP=40, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ca_custom, ca_customc,
MyBenchmark())