def construct(self, hyperparameters: Mapping, scenario: Mapping,
warm_startup_info: Mapping) -> None:
problem_init_params = HashableDict(
scenario['problem_initialization_parameters'])
problem = self._get_problem(scenario['Problem'], problem_init_params)
self.load_initial_solutions(warm_startup_info, problem)
from jmetal.operator.mutation import PermutationSwapMutation
termination_criterion_cls = self._get_class_from_module(
name=scenario["Budget"]["Type"], module=termination)
termination_criterion = termination_criterion_cls(
scenario["Budget"]["Amount"])
mh_name = hyperparameters["low level heuristic"].split(".")[1]
if mh_name == "GeneticAlgorithm":
from jmetal.algorithm.singleobjective.genetic_algorithm import (
GeneticAlgorithm)
from jmetal.operator.crossover import CXCrossover
from jmetal.operator.selection import BinaryTournamentSelection
self._llh_algorithm = GeneticAlgorithm(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PermutationSwapMutation(0.5),
crossover=CXCrossover(0.5),
selection=BinaryTournamentSelection(),
termination_criterion=termination_criterion,
population_generator=self._solution_generator)
elif mh_name == "SimulatedAnnealing":
from jmetal.algorithm.singleobjective.simulated_annealing import (
SimulatedAnnealing)
self._llh_algorithm = SimulatedAnnealing(
problem=problem,
mutation=PermutationSwapMutation(0.5),
termination_criterion=termination_criterion,
solution_generator=self._solution_generator)
elif mh_name == "EvolutionStrategy":
from jmetal.algorithm.singleobjective.evolution_strategy import (
EvolutionStrategy)
self._llh_algorithm = EvolutionStrategy(
problem=problem,
mu=500,
lambda_=500,
elitist=False,
mutation=PermutationSwapMutation(0.5),
termination_criterion=termination_criterion,
population_generator=self._solution_generator)
else:
self.logger.error(f"Wrong meta-heuristic name: {mh_name}")
from jmetal.algorithm.singleobjective.simulated_annealing import SimulatedAnnealing
from jmetal.operator import BitFlipMutation
from jmetal.problem import OneMax
from jmetal.util.solution import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = OneMax(number_of_bits=1024)
max_evaluations = 20000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=BitFlipMutation(probability=1.0 / problem.number_of_bits),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(
result, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm: ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Solution: ' + result.get_binary_string())
print('Fitness: ' + str(result.objectives[0]))
print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.algorithm.singleobjective.simulated_annealing import SimulatedAnnealing
from jmetal.operator import PolynomialMutation
from jmetal.problem.singleobjective.unconstrained import Rastrigin
from jmetal.util.observer import PrintObjectivesObserver
from jmetal.util.solutions_utils import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = Rastrigin(10)
max_evaluations = 250000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
objectives_observer = PrintObjectivesObserver(frequency=1000)
algorithm.observable.register(observer=objectives_observer)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(
result, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
from jmetal.algorithm.singleobjective.simulated_annealing import SimulatedAnnealing
from jmetal.operator import ScrambleMutation
from jmetal.problem import TSP
from jmetal.util.termination_criterion import StoppingByTime
if __name__ == "__main__":
problem = TSP(instance="resources/TSP_instances/kroA100.tsp")
print(f"Solving TSP problem with {problem.number_of_cities} cities.")
algorithm = SimulatedAnnealing(
problem=problem,
mutation=ScrambleMutation(probability=1.0 / problem.number_of_cities),
termination_criterion=StoppingByTime(max_seconds=10),
)
algorithm.run()
result = algorithm.get_result()
print(f"Algorithm: {algorithm.get_name()}")
print(f"Solution: {result.variables}")
print(f"The shortest path length: {str(result.objectives[0])}")
print(f"Computing time: {str(algorithm.total_computing_time)}")
print(f"Problem evaluations: {str(algorithm.evaluations)}")