def setUp(self):
self.D = 40
self.kh_custom = KrillHerdV4(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.kh_griewank = KrillHerdV4(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def simple_example(runs=10, D=10, nFES=50000):
for i in range(runs):
algo = KrillHerdV4(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 test_type_parametes(self):
d = KrillHerdV4.typeParameters()
self.assertIsNotNone(d.get('N_max', None))
self.assertIsNotNone(d.get('V_f', None))
self.assertIsNotNone(d.get('D_max', None))
self.assertIsNotNone(d.get('C_t', None))
self.assertIsNotNone(d.get('W_n', None))
self.assertIsNotNone(d.get('W_f', None))
self.assertIsNotNone(d.get('d_s', None))
self.assertIsNotNone(d.get('nn', None))
self.assertIsNone(d.get('Cr', None))
self.assertIsNone(d.get('Mu', None))
self.assertIsNone(d.get('epsilon', None))
def Krill_Herd_Optimization_4(matrix):
task = StoppingTask(D=6305, nGEN=1, benchmark=Qing())
scaler = StandardScaler()
matrix = scaler.fit_transform(matrix)
#matrix=np.transpose(matrix)
kh=KrillHerdV4(NP=3001)
KH, KH_f, d = initPopulation_KH(task, matrix, kh)
#print(KH)
#print(KH_f)
#print(KH_f.shape)
#print(d)
xb, fxb=getBest_algo(KH,KH_f)
print(xb)
#print(xb.shape)
print(fxb)
xb, fxb=run_algo(kh, task, KH, KH_f, xb, fxb, d['W_n'], d['W_f'], d['N'], d['F'])
return xb,fxb
def test_griewank_works_fine(self):
kh_griewank = KrillHerdV4(n=10, C_a=5, C_r=0.5, seed=self.seed)
kh_griewankc = KrillHerdV4(n=10, C_a=5, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, kh_griewank, kh_griewankc)
def test_custom_works_fine(self):
kh_custom = KrillHerdV4(n=10, C_a=2, C_r=0.5, seed=self.seed)
kh_customc = KrillHerdV4(n=10, C_a=2, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, kh_custom, kh_customc,
MyBenchmark())
def plot_example(D=10, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = KrillHerdV4(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=10, nFES=50000):
task = TaskConvPrint(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = KrillHerdV4(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 KrillHerdV4
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=50, benchmark=Sphere())
algo = KrillHerdV4(NP=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