def test_griewank_works_fine(self):
de_griewank = DynNpMultiStrategyDifferentialEvolution(NP=10,
CR=0.5,
F=0.9,
seed=self.seed)
de_griewankc = DynNpMultiStrategyDifferentialEvolution(NP=10,
CR=0.5,
F=0.9,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_griewank, de_griewankc,
'griewank')
def test_custom_works_fine(self):
de_custom = DynNpMultiStrategyDifferentialEvolution(NP=40,
F=0.5,
CR=0.9,
seed=self.seed)
de_customc = DynNpMultiStrategyDifferentialEvolution(NP=40,
F=0.5,
CR=0.9,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_custom, de_customc,
MyBenchmark())
def test_typeParameters(self):
d = DynNpMultiStrategyDifferentialEvolution.typeParameters()
self.assertTrue(d['rp'](10))
self.assertTrue(d['rp'](10.10))
self.assertFalse(d['rp'](0))
self.assertFalse(d['rp'](-10))
self.assertTrue(d['pmax'](10))
self.assertFalse(d['pmax'](0))
self.assertFalse(d['pmax'](-10))
self.assertFalse(d['pmax'](10.12))
def test_griewank_works_fine(self):
de_griewank = DynNpMultiStrategyDifferentialEvolution(
NP=10,
D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
CR=0.5,
F=0.9,
benchmark='griewank',
seed=self.seed)
de_griewankc = DynNpMultiStrategyDifferentialEvolution(
NP=10,
D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
CR=0.5,
F=0.9,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_griewank, de_griewankc)
def test_Custom_works_fine(self):
de_custom = DynNpMultiStrategyDifferentialEvolution(
D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
CR=0.9,
benchmark=MyBenchmark(),
seed=self.seed)
de_customc = DynNpMultiStrategyDifferentialEvolution(
D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
CR=0.9,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_custom, de_customc)
# 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 DynNpMultiStrategyDifferentialEvolution
from NiaPy.algorithms.basic.de import CrossBest2, CrossCurr2Best1
from NiaPy.util import OptimizationType
from NiaPy.task.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=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = DynNpMultiStrategyDifferentialEvolution(NP=80,
F=0.2,
CR=0.7,
strategies=(CrossCurr2Best1,
CrossBest2),
pmax=5)
best = algo.run(task=task)
print('%s -> %s' % (best[0].x, best[1]))
def test_algorithm_info(self):
self.assertIsNotNone(
DynNpMultiStrategyDifferentialEvolution.algorithmInfo())