def test_custom(self):
wvcpso_custom = ParticleSwarmAlgorithm(population_size=10,
c1=2.0,
c2=2.0,
w=0.7,
min_velocity=-4,
max_velocity=4,
seed=self.seed)
wvcpso_customc = ParticleSwarmAlgorithm(population_size=10,
c1=2.0,
c2=2.0,
w=0.7,
min_velocity=-4,
max_velocity=4,
seed=self.seed)
AlgorithmTestCase.test_algorithm_run(self, wvcpso_custom,
wvcpso_customc, MyProblem())
def test_griewank(self):
wvcpso_griewank = ParticleSwarmAlgorithm(population_size=10,
c1=2.0,
c2=2.0,
w=0.7,
min_velocity=-4,
max_velocity=4,
seed=self.seed)
wvcpso_griewankc = ParticleSwarmAlgorithm(population_size=10,
c1=2.0,
c2=2.0,
w=0.7,
min_velocity=-4,
max_velocity=4,
seed=self.seed)
AlgorithmTestCase.test_algorithm_run(self, wvcpso_griewank,
wvcpso_griewankc)
def test_algorithm_info(self):
al = ParticleSwarmAlgorithm.info()
self.assertIsNotNone(al)
# 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 ParticleSwarmAlgorithm
from niapy.task import Task, OptimizationType
from niapy.problems import Sphere
# we will run ParticleSwarmAlgorithm for 5 independent runs
for i in range(5):
task = Task(problem=Sphere(dimension=10),
max_evals=10000,
optimization_type=OptimizationType.MAXIMIZATION)
algo = ParticleSwarmAlgorithm(population_size=40,
c1=2.0,
c2=2.0,
w=0.7,
min_velocity=-4,
max_velocity=4)
best = algo.run(task=task)
print(best[-1])
from niapy.algorithms.basic import (GreyWolfOptimizer, ParticleSwarmAlgorithm)
from niapy.problems import (Problem, Ackley, Griewank, Sphere, HappyCat)
"""Example demonstrating the use of niapy Runner."""
class MyProblem(Problem):
def __init__(self, dimension, lower=-10, upper=10, *args, **kwargs):
super().__init__(dimension, lower, upper, *args, **kwargs)
def _evaluate(self, x):
return np.sum(x**2)
runner = Runner(dimension=40,
max_evals=100,
runs=2,
algorithms=[
GreyWolfOptimizer(), "FlowerPollinationAlgorithm",
ParticleSwarmAlgorithm(), "HybridBatAlgorithm",
"SimulatedAnnealing", "CuckooSearch"
],
problems=[
Ackley(dimension=40),
Griewank(dimension=40),
Sphere(dimension=40),
HappyCat(dimension=40), "rastrigin",
MyProblem(dimension=40)
])
runner.run(export='dataframe', verbose=True)
# 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 ParticleSwarmAlgorithm
from niapy.task import Task
from niapy.problems import Griewank
# we will run ParticleSwarmAlgorithm for 5 independent runs
algo = ParticleSwarmAlgorithm(population_size=100,
min_velocity=-4.0,
max_velocity=4.0)
for i in range(5):
task = Task(problem=Griewank(dimension=10, lower=-600, upper=600),
max_evals=10000)
best = algo.run(task=task)
print('%s -> %f' % (best[0], best[1]))
print(algo.get_parameters())
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
# 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 ParticleSwarmAlgorithm
from niapy.task import Task
from niapy.problems import Sphere
import numpy as np
def my_init(task, population_size, rng, **_kwargs):
pop = 0.2 + rng.random((population_size, task.dimension)) * task.range
fpop = np.apply_along_axis(task.eval, 1, pop)
return pop, fpop
# we will run Particle Swarm Algorithm with custom Init function for 5 independent runs
for i in range(5):
task = Task(problem=Sphere(dimension=10), max_evals=1000)
algo = ParticleSwarmAlgorithm(population_size=10, c1=2.0, c2=2.0, w=0.7, min_velocity=-4, max_velocity=4, initialization_function=my_init)
best = algo.run(task=task)
print(best)