class GSOTestCase(TestCase):
def setUp(self):
self.D = 40
self.gso_custom = GlowwormSwarmOptimization(D=self.D,
nFES=1000,
NP=35,
a=7,
Rmin=0.1,
Rmax=3,
benchmark=MyBenchmark())
self.gso_griewank = GlowwormSwarmOptimization(D=self.D,
nFES=1000,
NP=10,
a=5,
Rmin=0.01,
Rmax=3,
benchmark=Griewank())
def test_custom_works_fine(self):
fun = MyBenchmark().function()
x = self.gso_custom.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.D, x[0]), x[1], delta=1e2)
def test_griewank_works_fine(self):
fun = Griewank().function()
x = self.gso_griewank.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.D, x[0]), x[1], delta=1e2)
def test_griewank_works_fine(self):
gso_griewank = GlowwormSwarmOptimization(NP=10,
a=5,
Rmin=0.01,
Rmax=3,
seed=self.seed)
gso_griewankc = GlowwormSwarmOptimization(NP=10,
a=5,
Rmin=0.01,
Rmax=3,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, gso_griewank, gso_griewankc)
def test_custom_works_fine(self):
gso_custom = GlowwormSwarmOptimization(NP=35,
a=7,
Rmin=0.1,
Rmax=3,
seed=self.seed)
gso_customc = GlowwormSwarmOptimization(NP=35,
a=7,
Rmin=0.1,
Rmax=3,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, gso_custom, gso_customc,
MyBenchmark())
def logging_example(D=10, nFES=50000):
task = TaskConvPrint(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = GlowwormSwarmOptimization(n=25,
nt=5,
l0=5,
rho=0.2,
gamma=0.3,
beta=0.05,
s=0.05,
seed=None,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def plot_example(D=10, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = GlowwormSwarmOptimization(n=50,
nt=5,
l0=5,
rho=0.4,
gamma=0.6,
beta=0.08,
s=0.03,
seed=None,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def simple_example(runs=10, D=10, nFES=50000):
for i in range(runs):
algo = GlowwormSwarmOptimization(D=D,
nFES=nFES,
n=50,
nt=5,
l0=5,
rho=0.4,
gamma=0.6,
beta=0.08,
s=0.3,
seed=None,
benchmark=MyBenchmark())
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def setUp(self):
self.D = 40
self.gso_custom = GlowwormSwarmOptimization(D=self.D,
nFES=1000,
NP=35,
a=7,
Rmin=0.1,
Rmax=3,
benchmark=MyBenchmark())
self.gso_griewank = GlowwormSwarmOptimization(D=self.D,
nFES=1000,
NP=10,
a=5,
Rmin=0.01,
Rmax=3,
benchmark=Griewank())
def test_type_parameters_fine(self):
d = GlowwormSwarmOptimization.typeParameters()
self.assertIsNotNone(d.get('n', None))
self.assertIsNotNone(d.get('l0', None))
self.assertIsNotNone(d.get('nt', None))
self.assertIsNotNone(d.get('rho', None))
self.assertIsNotNone(d.get('gamma', None))
self.assertIsNotNone(d.get('beta', None))
self.assertIsNotNone(d.get('s', None))
# 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 GlowwormSwarmOptimization
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Glowworm Swarm Optimization for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nFES=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = GlowwormSwarmOptimization()
best = algo.run(task=task)
print(best)
def test_algorithm_info_fine(self): self.assertIsNotNone(GlowwormSwarmOptimization.algorithmInfo())
def test_custom_works_fine(self): gso_custom = GlowwormSwarmOptimization(D=self.D, nFES=self.nFES, nGEN=self.nGEN, NP=35, a=7, Rmin=0.1, Rmax=3, benchmark=MyBenchmark(), seed=self.seed) gso_customc = GlowwormSwarmOptimization(D=self.D, nFES=self.nFES, nGEN=self.nGEN, NP=35, a=7, Rmin=0.1, Rmax=3, benchmark=MyBenchmark(), seed=self.seed) AlgorithmTestCase.algorithm_run_test(self, gso_custom, gso_customc)
def test_griewank_works_fine(self): gso_griewank = GlowwormSwarmOptimization(D=self.D, nFES=self.nFES, nGEN=self.nGEN, NP=10, a=5, Rmin=0.01, Rmax=3, benchmark=Griewank(), seed=self.seed) gso_griewankc = GlowwormSwarmOptimization(D=self.D, nFES=self.nFES, nGEN=self.nGEN, NP=10, a=5, Rmin=0.01, Rmax=3, benchmark=Griewank(), seed=self.seed) AlgorithmTestCase.algorithm_run_test(self, gso_griewank, gso_griewankc)
