from jmetal.algorithm.multiobjective.random_search import RandomSearch
from jmetal.problem import ZDT1
from jmetal.util.solution import (
print_function_values_to_file,
print_variables_to_file,
read_solutions,
)
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = ZDT1()
problem.reference_front = read_solutions(
filename="resources/reference_front/ZDT1.pf")
max_evaluations = 1000
algorithm = RandomSearch(problem=problem,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
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}")
from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
from jmetal.problem import DTLZ2
from jmetal.util.aggregative_function import Tschebycheff
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 = DTLZ2()
problem.reference_front = read_solutions(filename='resources/reference_front/DTLZ2.3D.pf')
max_evaluations = 50000
algorithm = MOEAD(
problem=problem,
population_size=300,
crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),
neighbor_size=20,
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2,
weight_files_path='resources/MOEAD_weights',
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
from jmetal.problem import LZ09_F2
from jmetal.util.aggregative_function import Tschebycheff
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 = LZ09_F2()
problem.reference_front = read_solutions(
filename='resources/reference_front/LZ09_F2.pf')
max_evaluations = 150000
algorithm = MOEAD(
problem=problem,
population_size=300,
crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
aggregative_function=Tschebycheff(
dimension=problem.number_of_objectives),
neighbor_size=20,
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2,
weight_files_path='resources/MOEAD_weights',
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.run()
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import Srinivas
from jmetal.util.comparator import DominanceComparator
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 = Srinivas()
problem.reference_front = read_solutions(
filename='resources/reference_front/Srinivas.pf')
max_evaluations = 25000
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=max_evaluations),
dominance_comparator=DominanceComparator())
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)
def generate_summary_from_experiment(
input_dir: str,
quality_indicators: List[QualityIndicator],
reference_fronts: str = ""):
"""Compute a list of quality indicators. The input data directory *must* met the following structure (this is generated
automatically by the Experiment class):
* <base_dir>
* algorithm_a
* problem_a
* FUN.0.tsv
* FUN.1.tsv
* VAR.0.tsv
* VAR.1.tsv
* ...
:param input_dir: Directory where all the input data is found (function values and variables).
:param reference_fronts: Directory where reference fronts are found.
:param quality_indicators: List of quality indicators to compute.
:return: None.
"""
if not quality_indicators:
quality_indicators = []
with open("QualityIndicatorSummary.csv", "w+") as of:
of.write(
"Algorithm,Problem,ExecutionId,IndicatorName,IndicatorValue\n")
for dirname, _, filenames in os.walk(input_dir):
for filename in filenames:
try:
# Linux filesystem
algorithm, problem = dirname.split("/")[-2:]
except ValueError:
# Windows filesystem
algorithm, problem = dirname.split("\\")[-2:]
if "TIME" in filename:
run_tag = [s for s in filename.split(".") if s.isdigit()].pop()
with open(os.path.join(dirname, filename),
"r") as content_file:
content = content_file.read()
with open("QualityIndicatorSummary.csv", "a+") as of:
of.write(",".join(
[algorithm, problem, run_tag, "Time",
str(content)]))
of.write("\n")
if "FUN" in filename:
solutions = read_solutions(os.path.join(dirname, filename))
run_tag = [s for s in filename.split(".") if s.isdigit()].pop()
for indicator in quality_indicators:
reference_front_file = os.path.join(
reference_fronts, problem + ".pf")
# Add reference front if any
if hasattr(indicator, "reference_front"):
if Path(reference_front_file).is_file():
reference_front = []
with open(reference_front_file) as file:
for line in file:
reference_front.append(
[float(x) for x in line.split()])
indicator.reference_front = reference_front
else:
logger.warning("Reference front not found at",
reference_front_file)
result = indicator.compute([
solutions[i].objectives for i in range(len(solutions))
])
# Save quality indicator value to file
with open("QualityIndicatorSummary.csv", "a+") as of:
of.write(",".join([
algorithm, problem, run_tag,
indicator.get_short_name(),
str(result)
]))
of.write("\n")
