# 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.task import Task
from niapy.problems import Sphere
from niapy.algorithms.basic import DynamicFireworksAlgorithm
# we will run Fireworks Algorithm for 5 independent runs
algo = DynamicFireworksAlgorithm(population_size=70,
amplitude_init=0.1,
amplitude_final=0.9)
for i in range(5):
task = Task(problem=Sphere(dimension=10), max_iters=80)
best = algo.run(task)
print('%s -> %s' % (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 EvolutionStrategyMp1
from niapy.task import Task
from niapy.problems import Sphere
# we will run Differential Evolution for 5 independent runs
for i in range(5):
task = Task(problem=Sphere(dimension=10), max_evals=10000)
algo = EvolutionStrategyMp1()
best = algo.run(task)
print('%s -> %f' % (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 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])
# 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.task import Task
from niapy.algorithms.basic import DifferentialEvolution
from niapy.problems import Sphere
# 1 Number of function evaluations (nFES) as a stopping criteria
for i in range(10):
task = Task(problem=Sphere(dimension=10), max_evals=10000)
algo = DifferentialEvolution(population_size=40,
crossover_probability=0.9,
differential_weight=0.5)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print('---------------------------------------')
# 2 Number of generations (iterations) as a stopping criteria
for i in range(10):
task = Task(problem=Sphere(dimension=10), max_iters=1000)
algo = DifferentialEvolution(population_size=40,
crossover_probability=0.9,
differential_weight=0.5)
best = algo.run(task)
print('%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 ComprehensiveLearningParticleSwarmOptimizer
from niapy.problems import Sphere
from niapy.task import Task
# we will run ParticleSwarmAlgorithm for 5 independent runs
algo = ComprehensiveLearningParticleSwarmOptimizer(population_size=50,
c1=.3,
c2=1.0,
m=5,
w=0.86,
min_velocity=-2,
max_velocity=2)
for i in range(5):
task = Task(problem=Sphere(dimension=25), max_evals=20000)
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.task import Task
from niapy.problems import Sphere
from niapy.algorithms.other import RandomSearch
task = Task(problem=Sphere(dimension=5), max_iters=5000)
algo = RandomSearch()
best = algo.run(task=task)
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
from niapy.task import Task
from niapy.problems import Sphere
from niapy.algorithms.basic import DifferentialEvolution
# Storing improvements during the evolutionary cycle
task = Task(max_evals=10000, problem=Sphere(dimension=10))
algo = DifferentialEvolution(population_size=40, crossover_probability=0.9, differential_weight=0.5)
best = algo.run(task)
evals, x_f = task.convergence_data(x_axis='evals')
print(evals) # print function evaluations
print(x_f) # print values
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)
def test_FA_iters(self):
task = Task(max_iters=1000, problem=Sphere(10))
algo = FireflyAlgorithm(population_size=10)
algo.run_task(task)
iters = task.iters
self.assertEqual(1000, iters)
def test_FA_evals(self):
task = Task(max_evals=1000, problem=Sphere(10))
algo = FireflyAlgorithm(population_size=10)
algo.run_task(task)
evals = task.evals
self.assertEqual(1000, evals)
def test_BA_iters_to_fes(self):
task = Task(max_iters=1000, problem=Sphere(10))
algo = BatAlgorithm(population_size=10)
algo.run_task(task)
evals = task.evals
self.assertEqual(10010, evals)
def test_DE_iters(self):
task = Task(max_iters=1000, problem=Sphere(10))
algo = DifferentialEvolution(population_size=10, CR=0.9, F=0.5)
algo.run_task(task)
iters = task.iters
self.assertEqual(1000, iters)
# 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.task import Task
from niapy.problems import Sphere
from niapy.algorithms.basic import CatSwarmOptimization
task = Task(problem=Sphere(dimension=10), max_evals=1000, enable_logging=True)
algo = CatSwarmOptimization()
best = algo.run(task=task)
print('%s -> %s' % (best[0], best[1]))
# plot a convergence graph
task.plot_convergence(x_axis='evals')