from jmetal.algorithm.multiobjective.ibea import IBEA
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.solution import read_solutions, print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1()
problem.reference_front = read_solutions(
filename='resources/reference_front/ZDT1.pf')
algorithm = IBEA(problem=problem,
kappa=1.,
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(25000))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.' + algorithm.label)
print(f'Algorithm: ${algorithm.get_name()}')
print(f'Problem: ${problem.get_name()}')
print(f'Computing time: ${algorithm.total_computing_time}')
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