def test_griewank_works_fine(self):
dynnpjde_griewank = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark='griewank',
seed=self.seed)
dynnpjde_griewankc = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, dynnpjde_griewank,
dynnpjde_griewankc)
def test_custom_works_fine(self):
dynnpjde_custom = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark=MyBenchmark(),
seed=self.seed)
dynnpjde_customc = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, dynnpjde_custom,
dynnpjde_customc)
def test_typeParameters(self):
d = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm.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):
dynnpjde_griewank = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
dynnpjde_griewankc = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
AlgorithmTestCase.test_algorithm_run(self, dynnpjde_griewank,
dynnpjde_griewankc)
def test_custom_works_fine(self):
dynnpjde_custom = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
dynnpjde_customc = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(
NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
AlgorithmTestCase.test_algorithm_run(self, dynnpjde_custom,
dynnpjde_customc, MyBenchmark())
# 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.modified import DynNpSelfAdaptiveDifferentialEvolutionAlgorithm
from NiaPy.util 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=10000, optType=OptimizationType.MINIMIZATION, benchmark=Sphere())
algo = DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(NP=90, F=0.64, CR=0.9, pmax=25)
best = algo.run(task=task)
print('%s -> %s' % (best[0].x, best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3