class GSATestCase(TestCase):
def setUp(self):
self.gsa_custom = GravitationalSearchAlgorithm(D=10,
NP=40,
nFES=1000,
benchmark=MyBenchmark())
self.gsa_griewank = GravitationalSearchAlgorithm(NP=10,
D=40,
nFES=1000,
benchmark='griewank')
def test_Custom_works_fine(self):
fun = MyBenchmark().function()
x = self.gsa_custom.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.gsa_custom.task.D, asarray(x[0])),
x[1],
delta=1e2)
def test_griewank_works_fine(self):
fun = Griewank().function()
x = self.gsa_griewank.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.gsa_griewank.task.D, asarray(x[0])),
x[1],
delta=1e2)
def setUp(self):
self.gsa_custom = GravitationalSearchAlgorithm(D=10,
NP=40,
nFES=1000,
benchmark=MyBenchmark())
self.gsa_griewank = GravitationalSearchAlgorithm(NP=10,
D=40,
nFES=1000,
benchmark='griewank')
def test_Custom_works_fine(self):
gsa_custom = GravitationalSearchAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
benchmark=MyBenchmark(),
seed=self.seed)
gsa_customc = GravitationalSearchAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, gsa_custom, gsa_customc)
def test_griewank_works_fine(self):
gsa_griewank = GravitationalSearchAlgorithm(NP=10,
D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
benchmark='griewank',
seed=self.seed)
gsa_griewankc = GravitationalSearchAlgorithm(NP=10,
D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, gsa_griewank, gsa_griewankc)
def logging_example(D=10,
nFES=50000,
seed=None,
optType=OptimizationType.MINIMIZATION,
optFunc=MinMB):
task = TaskConvPrint(D=D,
nFES=nFES,
nGEN=50000,
optType=optType,
benchmark=optFunc())
algo = GravitationalSearchAlgorithm(NP=40,
F=0.5,
CR=0.9,
seed=seed,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def simple_example(runs=10,
D=10,
nFES=50000,
seed=None,
optType=OptimizationType.MINIMIZATION,
optFunc=MinMB):
for i in range(runs):
algo = GravitationalSearchAlgorithm(D=D,
NP=40,
nFES=nFES,
F=0.5,
CR=0.9,
seed=seed +
1 if seed != None else seed,
optType=optType,
benchmark=optFunc())
Best = algo.run()
logger.info('%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 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)
def test_Custom_works_fine(self):
gsa_custom = GravitationalSearchAlgorithm(NP=40, seed=self.seed)
gsa_customc = GravitationalSearchAlgorithm(NP=40, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, gsa_custom, gsa_customc,
MyBenchmark())
def test_griewank_works_fine(self):
gsa_griewank = GravitationalSearchAlgorithm(NP=10, seed=self.seed)
gsa_griewankc = GravitationalSearchAlgorithm(NP=10, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, gsa_griewank, gsa_griewankc)