class FPATestCase(TestCase):
def setUp(self):
self.fpa_custom = FlowerPollinationAlgorithm(10, 20, 10000, 0.5,
MyBenchmark())
self.fpa_griewank = FlowerPollinationAlgorithm(10, 20, 10000, 0.5,
'griewank')
def test_custom_works_fine(self):
self.assertTrue(self.fpa_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.fpa_griewank.run())
class FPATestCase(TestCase):
def setUp(self):
self.fpa_custom = FlowerPollinationAlgorithm(NP=10,
D=20,
nFES=1000,
p=0.5,
benchmark=MyBenchmark())
self.fpa_griewank = FlowerPollinationAlgorithm(D=10,
NP=20,
nFES=1000,
p=0.5,
benchmark='griewank')
self.fpa_beta_griewank = FlowerPollinationAlgorithm(
D=10, NP=20, nFES=1000, p=0.5, beta=1.2, benchmark='griewank')
def test_custom_works_fine(self):
self.assertTrue(self.fpa_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.fpa_griewank.run())
def test_griewank_works_fine_with_beta(self):
self.assertTrue(self.fpa_beta_griewank.run())
from NiaPy.algorithms.basic import FlowerPollinationAlgorithm
# we will run 10 repetitions of Grey Wolf Optimizer against Pinter benchmark function
for i in range(20):
# first parameter takes dimension of problem
# second parameter is population size
# third parameter takes the number of function evaluations
# fourth parameter is benchmark function
algorithm = FlowerPollinationAlgorithm(10, 20 , 10000,0.5, 'pinter')
# running algorithm returns best found minimum
best = algorithm.run()
# printing best minimum
print(best)
# 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.basic import FlowerPollinationAlgorithm
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
#we will run Flower Pollination Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nFES=10000, optType=OptimizationType.MINIMIZATION, benchmark=Sphere())
algo = FlowerPollinationAlgorithm(NP=20, p=0.5)
best = algo.run(task=task)
print(best)
import random
import logging
from NiaPy.algorithms.basic import FlowerPollinationAlgorithm
logging.basicConfig()
logger = logging.getLogger('examples')
logger.setLevel('INFO')
# For reproducive results
random.seed(1234)
class MyBenchmark(object):
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 = val + sol[i] * sol[i]
return val
return evaluate
for i in range(10):
Algorithm = FlowerPollinationAlgorithm(NP=10, D=20, nFES=10000, p=0.5, benchmark=MyBenchmark())
Best = Algorithm.run()
logger.info(Best)