def test_griewank_works_fine(self):
mts_griewank = MultipleTrajectorySearchV1(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
mts_griewankc = MultipleTrajectorySearchV1(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, mts_griewank, mts_griewankc)
def test_custom_works_fine(self):
mts_custom = MultipleTrajectorySearchV1(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
mts_customc = MultipleTrajectorySearchV1(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, mts_custom, mts_customc,
MyBenchmark())
def setUp(self):
self.D = 40
self.mts_custom = MultipleTrajectorySearchV1(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.mts_griewank = MultipleTrajectorySearchV1(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def test_griewank_works_fine(self):
mts_griewank = MultipleTrajectorySearchV1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
mts_griewankc = MultipleTrajectorySearchV1(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, mts_griewank, mts_griewankc)
def test_custom_works_fine(self):
mts_custom = MultipleTrajectorySearchV1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
mts_customc = MultipleTrajectorySearchV1(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, mts_custom, mts_customc)
def logging_example(D=10,
nFES=50000,
nGEN=100000,
seed=None,
optType=OptimizationType.MINIMIZATION,
optFunc=MinMB,
**kn):
task = TaskConvPrint(D=D,
nFES=nFES,
nGEN=nGEN,
optType=optType,
benchmark=optFunc())
algo = MultipleTrajectorySearchV1(task=task, n=15, C_a=1, C_r=0.5)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
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 = MultipleTrajectorySearchV1(D=D,
nFES=nFES,
nGEN=nGEN,
n=15,
C_a=1,
C_r=0.5,
optType=optType,
benchmark=optFunc())
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
import random
from NiaPy.algorithms.other import MultipleTrajectorySearchV1
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Nelder Mead algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nGEN=40,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = MultipleTrajectorySearchV1()
best = algo.run(task=task)
print('%s -> %s' % (best[0], best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3