def configure_experiment(problems: dict, n_run: int):
jobs = []
max_evaluations = 25000
for run in range(n_run):
for problem_tag, problem in problems.items():
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(
probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0,
distribution_index=20),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
),
algorithm_tag="NSGAII",
problem_tag=problem_tag,
run=run,
))
jobs.append(
Job(
algorithm=GDE3(
problem=problem,
population_size=100,
cr=0.5,
f=0.5,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
),
algorithm_tag="GDE3",
problem_tag=problem_tag,
run=run,
))
jobs.append(
Job(
algorithm=SMPSO(
problem=problem,
swarm_size=100,
mutation=PolynomialMutation(
probability=1.0 / problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
),
algorithm_tag="SMPSO",
problem_tag=problem_tag,
run=run,
))
return jobs
def configure_experiment(problems: dict, n_run: int):
jobs = []
for run in range(n_run):
for problem_tag, problem in problems.items():
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_size=20,
offspring_population_size=20,
mutation=IntegerPolynomialMutation(probability=0.05, distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.3, distribution_index=20),
# termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
termination_criterion=stopCriterion
),
algorithm_tag='NSGAII',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=SPEA2(
problem=problem,
population_size=20,
offspring_population_size=20,
mutation=IntegerPolynomialMutation(probability=0.05, distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.3, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
),
algorithm_tag='SPEA2',
problem_tag=problem_tag,
run=run,
)
)
return jobs
def configure_experiment(problems: dict, n_run: int):
jobs = []
max_evaluations = 25000
for run in range(n_run):
for problem_tag, problem in problems.items():
jobs.append(
Job(
algorithm=DynamicNSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
),
algorithm_tag='DynamicNSGAII',
problem_tag=problem_tag,
run=run,
)
)
return jobs
def configure_experiment(problems: dict, n_run: int):
"""Configures the experiments
Args:
problems (dict): The MEWpy optimization problem
n_run (int): the number of runs of each MOEA
Returns:
a list of jobs
"""
from mewpy.optimization.jmetal.operators import UniformCrossoverOU, GrowMutationOU, ShrinkMutation, \
SingleMutationOU, MutationContainer
crossover = UniformCrossoverOU(0.5, max_size=candidate_max_size)
mutators = []
mutators.append(GrowMutationOU(1.0, max_size=candidate_max_size))
mutators.append(ShrinkMutation(1.0, min_size=candidate_max_size))
mutators.append(SingleMutationOU(1.0))
mutation = MutationContainer(0.3, mutators=mutators)
jobs = []
for run in range(n_run):
for problem_tag, problem in problems.items():
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_evaluator=MultiprocessEvaluator(N_CPU),
population_size=100,
offspring_population_size=100,
mutation=mutation,
crossover=crossover,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
algorithm_tag='NSGAII',
problem_tag=problem_tag,
run=run,
))
jobs.append(
Job(algorithm=SPEA2(
problem=problem,
population_evaluator=MultiprocessEvaluator(N_CPU),
population_size=100,
offspring_population_size=100,
mutation=mutation,
crossover=crossover,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
algorithm_tag='SPEA2',
problem_tag=problem_tag,
run=run))
jobs.append(
Job(
algorithm=IBEA(
problem=problem,
population_evaluator=MultiprocessEvaluator(N_CPU),
kappa=1.,
population_size=100,
offspring_population_size=100,
mutation=mutation,
crossover=crossover,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
algorithm_tag='IBEA',
problem_tag=problem_tag,
run=run,
))
return jobs
def configure_experiment(problems: dict, n_run: int):
jobs = []
num_processes = config.num_cpu
population = 10
generations = 10
max_evaluations = population * generations
for run in range(n_run):
for problem_tag, problem in problems.items():
reference_point = FloatSolution([0, 0], [1, 1], problem.number_of_objectives, )
reference_point.objectives = np.repeat(1, problem.number_of_objectives).tolist()
jobs.append(
Job(
algorithm=NSGAIII(
problem=problem,
population_size=population,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
reference_directions=UniformReferenceDirectionFactory(4, n_points=100),
),
algorithm_tag='NSGAIII',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=GDE3(
problem=problem,
population_size=population,
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
cr=0.5,
f=0.5,
),
algorithm_tag='GDE3',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=SPEA2(
problem=problem,
population_size=population,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
offspring_population_size=population,
),
algorithm_tag='SPEA2',
problem_tag=problem_tag,
run=run,
)
)
return jobs
def configure_experiment(problems: dict, n_run: int):
jobs = []
for run in range(n_run):
for problem_tag, problem in problems.items():
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_size=POPULATION_SIZE,
offspring_population_size=POPULATION_SIZE,
mutation=IntegerPolynomialMutation(probability=0.05, distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.3, distribution_index=20),
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations, reference_point=REFERENCE_POINT,
AlgorithmName='NSGAII')
# termination_criterion=stopCriterion
),
algorithm_tag='NSGAII',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=NSGAIII(
problem=problem,
population_size=POPULATION_SIZE,
mutation=IntegerPolynomialMutation(probability=0.05, distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.3, distribution_index=20),
reference_directions=UniformReferenceDirectionFactory(2, n_points=91),
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations,
reference_point=REFERENCE_POINT,
AlgorithmName='NSGAIII')
# termination_criterion=stopCriterion
),
algorithm_tag='NSGAIII',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=SPEA2(
problem=problem,
population_size=POPULATION_SIZE,
offspring_population_size=POPULATION_SIZE,
mutation=IntegerPolynomialMutation(probability=0.05, distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.3, distribution_index=20),
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations, reference_point=REFERENCE_POINT,
AlgorithmName='SPEA2')
),
algorithm_tag='SPEA2',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=HYPE(
problem=problem,
reference_point=reference_point,
population_size=POPULATION_SIZE,
offspring_population_size=POPULATION_SIZE,
mutation=IntegerPolynomialMutation(probability=0.05, distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.3, distribution_index=20),
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations, reference_point=REFERENCE_POINT,
AlgorithmName='HYPE')
),
algorithm_tag='HYPE',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=MOCell(
problem=problem,
population_size=POPULATION_SIZE,
neighborhood=C9(4, 4),
archive=CrowdingDistanceArchive(100),
mutation=IntegerPolynomialMutation(probability=0.05, distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.3, distribution_index=20),
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations, reference_point=REFERENCE_POINT,
AlgorithmName='MOCell')
),
algorithm_tag='MOCELL',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=OMOPSO(
problem=problem,
swarm_size=swarm_size,
epsilon=0.0075,
uniform_mutation=UniformMutation(probability=0.05, perturbation=0.5),
non_uniform_mutation=NonUniformMutation(mutation_probability, perturbation=0.5,
max_iterations=int(max_evaluations / swarm_size)),
leaders=CrowdingDistanceArchive(10),
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations,
reference_point=REFERENCE_POINT,
AlgorithmName='OMOPSO')
),
algorithm_tag='OMOPSO',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=SMPSO(
problem=problem,
swarm_size=POPULATION_SIZE,
mutation=PolynomialMutation(probability=0.05, distribution_index=20),
leaders=CrowdingDistanceArchive(20),
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations,
reference_point=REFERENCE_POINT,
AlgorithmName='SMPSO')
),
algorithm_tag='SMPSO',
problem_tag=problem_tag,
run=run,
)
)
return jobs