def __init__(self, islands_len, iterval, migration_rate, random_groups, random_destination, max_evaluations, problem):
self.islands_len = islands_len
self.iterval = iterval
self.max_evaluations = max_evaluations
self.problem = problem
self.migration = Migration(
migration_rate, random_groups, random_destination)
for i in range(islands_len):
# possible to override problem later self.problem
self.islands[str(i)] = GeneticAlgorithm(
problem=self.problem,
population_size=100,
offspring_population_size=100,
mutation=UniformMutation(0.006, 20.0),
crossover=SBXCrossover(0.3, 19.0),
selection=BinaryTournamentSelection(),
termination_criterion=StoppingByEvaluations(
max_evaluations=iterval)
)
print(self.islands)
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1Modified()
problem.reference_front = read_solutions(
filename='../../resources/reference_front/{}.pf'.format(
problem.get_name()))
mutation_probability = 1.0 / problem.number_of_variables
max_evaluations = 100
swarm_size = 10
algorithm = OMOPSO(
problem=problem,
swarm_size=swarm_size,
epsilon=0.0075,
uniform_mutation=UniformMutation(probability=mutation_probability,
perturbation=0.5),
non_uniform_mutation=NonUniformMutation(
mutation_probability,
perturbation=0.5,
max_iterations=max_evaluations / swarm_size),
leaders=CrowdingDistanceArchive(10),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
swarm_evaluator=SparkEvaluator(),
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
def get_algorithm_instance(algo_name):
algos = {
'smpso':
SMPSO(problem=objective_function,
swarm_size=swarm_size,
mutation=PolynomialMutation(probability=mutation_probability,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
'omopso':
OMOPSO(problem=objective_function,
swarm_size=swarm_size,
epsilon=0.0075,
uniform_mutation=UniformMutation(
probability=mutation_probability, perturbation=0.5),
non_uniform_mutation=NonUniformMutation(
mutation_probability,
perturbation=0.5,
max_iterations=int(max_evaluations / swarm_size)),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
'nsgaii':
NSGAII(problem=objective_function,
population_size=30,
offspring_population_size=30,
mutation=PolynomialMutation(probability=mutation_probability,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
'spea2':
SPEA2(problem=objective_function,
population_size=30,
offspring_population_size=30,
mutation=PolynomialMutation(probability=mutation_probability,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
'moead':
MOEAD(
problem=objective_function,
population_size=30,
crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
mutation=PolynomialMutation(probability=mutation_probability,
distribution_index=20),
aggregative_function=Tschebycheff(
dimension=objective_function.number_of_objectives),
neighbor_size=5,
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2,
weight_files_path='resources/MOEAD_weights',
termination_criterion=StoppingByEvaluations(max_evaluations=700)),
'ibea':
IBEA(problem=objective_function,
kappa=1.0,
population_size=30,
offspring_population_size=30,
mutation=PolynomialMutation(probability=mutation_probability,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations))
}
return algos[algo_name]
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
