class EFWATestCase(TestCase):
def setUp(self):
self.D = 40
self.efwa_custom = EnhancedFireworksAlgorithm(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.efwa_griewank = EnhancedFireworksAlgorithm(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def test_custom_works_fine(self):
fun = MyBenchmark().function()
x = self.efwa_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.efwa_griewank.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.D, x[0]), x[1], delta=1e2)
def test_griewank_works_fine(self):
fwa_griewank = EnhancedFireworksAlgorithm(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
fwa_griewankc = EnhancedFireworksAlgorithm(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_griewank, fwa_griewankc)
def test_custom_works_fine(self):
fwa_custom = EnhancedFireworksAlgorithm(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
fwa_customc = EnhancedFireworksAlgorithm(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_custom, fwa_customc,
MyBenchmark())
def setUp(self):
self.D = 40
self.efwa_custom = EnhancedFireworksAlgorithm(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.efwa_griewank = EnhancedFireworksAlgorithm(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def test_custom_works_fine(self):
fwa_custom = EnhancedFireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
fwa_customc = EnhancedFireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_custom, fwa_customc)
def test_griewank_works_fine(self):
fwa_griewank = EnhancedFireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
fwa_griewankc = EnhancedFireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_griewank, fwa_griewankc)
# 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 EnhancedFireworksAlgorithm
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Sphere
# we will run Fireworks Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nGEN=100, benchmark=Sphere())
algo = EnhancedFireworksAlgorithm(N=70, Ainit=0.1, Afinal=0.9)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
def plot_example(D=10, nFES=50000, nGEN=100000, seed=None, optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc())
algo = EnhancedFireworksAlgorithm(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, nGEN=10000, seed=None, optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn):
for i in range(runs):
algo = EnhancedFireworksAlgorithm(D=D, nFES=nFES, optType=optType, benchmark=optFunc())
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))