class BBFWATestCase(TestCase):
def setUp(self):
self.D = 40
self.bbfa_custom = BareBonesFireworksAlgorithm(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.bbfa_griewank = BareBonesFireworksAlgorithm(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.bbfa_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.bbfa_griewank.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.D, x[0]), x[1], delta=1e2)
def simple_example(runs=10, D=10, nFES=50000):
for i in range(runs):
algo = BareBonesFireworksAlgorithm(D=D,
nFES=nFES,
n=15,
C_a=1,
C_r=0.5,
benchmark=MyBenchmark())
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def setUp(self):
self.D = 40
self.bbfwa_custom = BareBonesFireworksAlgorithm(
D=self.D, nFES=1000, n=10, C_a=2, C_r=0.5, benchmark=MyBenchmark())
self.bbfwa_griewank = BareBonesFireworksAlgorithm(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def test_custom_works_fine(self):
bbfwa_custom = BareBonesFireworksAlgorithm(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
bbfwa_customc = BareBonesFireworksAlgorithm(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, bbfwa_custom, bbfwa_customc,
MyBenchmark())
def test_griewank_works_fine(self):
bbfwa_griewank = BareBonesFireworksAlgorithm(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
bbfwa_griewankc = BareBonesFireworksAlgorithm(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, bbfwa_griewank,
bbfwa_griewankc)
def test_custom_works_fine(self):
bbfwa_custom = BareBonesFireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
bbfwa_customc = BareBonesFireworksAlgorithm(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, bbfwa_custom, bbfwa_customc)
def test_griewank_works_fine(self):
bbfwa_griewank = BareBonesFireworksAlgorithm(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
bbfwa_griewankc = BareBonesFireworksAlgorithm(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, bbfwa_griewank,
bbfwa_griewankc)
def plot_example(D=100, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = BareBonesFireworksAlgorithm(task=task, n=15, C_a=1, C_r=0.5)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def logging_example(D=100, nFES=50000):
task = TaskConvPrint(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = BareBonesFireworksAlgorithm(task=task, n=15, C_a=1, C_r=0.5)
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 BareBonesFireworksAlgorithm
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Fireworks Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nFES=1000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = BareBonesFireworksAlgorithm()
best = algo.run(task=task)
print('%s -> %s' % (best[0], best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
def __init__(self):
self.Lower = -11
self.Upper = 11
def function(self):
def evaluate(D, sol):
val = 0.0
for i in range(D):
val += sol[i]**2
return val
return evaluate
for i in range(10):
algo = BareBonesFireworksAlgorithm(D=50,
nFES=50000,
n=15,
C_a=1,
C_r=0.5,
benchmark=MyBenchmark())
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
# dodatek
# import numpy as np
# logger.info('%s' % (MyBenchmark().function()(50, best[0])))
# print (best[2])
# logger.info('%s' % np.apply_along_axis(lambda x: np.sum(x ** 2), 1, best[2]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3