if __name__ == '__main__': problem = Rastrigin(10) max_evaluations = 50000 algorithm = NSGAII( problem=problem, population_size=100, offspring_population_size=100, mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20.0), crossover=SBXCrossover(probability=0.9, distribution_index=20.0), termination_criterion=StoppingByEvaluations(max=max_evaluations), dominance_comparator=DominanceComparator()) algorithm.observable.register(observer=PrintObjectivesObserver(1000)) algorithm.run() front = algorithm.get_result() # Save results to file print_function_values_to_file( front, 'FUN.' + algorithm.get_name() + "-" + problem.get_name()) print_variables_to_file( front, 'VAR.' + algorithm.get_name() + "-" + problem.get_name()) print('Algorithm (continuous problem): ' + algorithm.get_name()) print('Problem: ' + problem.get_name()) print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.algorithm.singleobjective.local_search import LocalSearch from jmetal.operator import PolynomialMutation from jmetal.problem.singleobjective.unconstrained import Rastrigin 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 = Rastrigin(10) max_evaluations = 100000 algorithm = LocalSearch( problem=problem, mutation=PolynomialMutation(1.0 / problem.number_of_variables, 20.0), termination_criterion=StoppingByEvaluations(max_evaluations=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: " + str(result.variables)) print("Fitness: " + str(result.objectives[0])) print("Computing time: " + str(algorithm.total_computing_time))
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()) print('Algorithm: ' + algorithm.get_name()) print('Problem: ' + problem.get_name()) print('Solution: ' + str(result.variables[0])) print('Fitness: ' + str(result.objectives[0])) print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.problem.singleobjective.unconstrained import Rastrigin from jmetal.util.observer import PrintObjectivesObserver from jmetal.util.solution_list 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 = 100000 algorithm = LocalSearch( problem=problem, mutation=PolynomialMutation(1.0 / problem.number_of_variables, 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()) print('Algorithm: ' + algorithm.get_name()) print('Problem: ' + problem.get_name()) print('Solution: ' + str(result.variables)) print('Fitness: ' + str(result.objectives[0])) print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.operator import PolynomialMutation, SBXCrossover from jmetal.problem.singleobjective.unconstrained import Rastrigin from jmetal.util.termination_criterion import StoppingByEvaluations from main import Emas if __name__ == '__main__': problem = Rastrigin(10) algorithm = Emas( number_of_islands=10, init_island_population_size=20, problem=problem, mutation=PolynomialMutation(1.0 / problem.number_of_variables, 0.2), crossover=SBXCrossover(0.9, 20.0), termination_criterion=StoppingByEvaluations(max_evaluations=20000)) algorithm.run() result = algorithm.get_result() print('Algorithm: {}'.format(algorithm.get_name())) print('Problem: {}'.format(problem.get_name())) for x in result: for z in x: print('Solution: {}'.format(z.solution.variables)) print('Fitness: {}'.format(z.solution.objectives[0])) print('Energy: {}'.format(z.energy)) print('Computing time: {}'.format(algorithm.total_computing_time)) print(len(result)) print("Best fitness: " + str(min([z.fitness() for x in result for z in x])))
from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm from jmetal.operator import BinaryTournamentSelection, PolynomialMutation, SBXCrossover from jmetal.problem.singleobjective.unconstrained import Rastrigin from jmetal.util.termination_criterion import StoppingByEvaluations if __name__ == "__main__": problem = Rastrigin(10) algorithm = GeneticAlgorithm( problem=problem, population_size=100, offspring_population_size=1, mutation=PolynomialMutation(1.0 / problem.number_of_variables, 20.0), crossover=SBXCrossover(0.9, 5.0), selection=BinaryTournamentSelection(), termination_criterion=StoppingByEvaluations(max_evaluations=100000), ) algorithm.run() result = algorithm.get_result() print("Algorithm: {}".format(algorithm.get_name())) print("Problem: {}".format(problem.get_name())) print("Solution: {}".format(result.variables)) print("Fitness: {}".format(result.objectives[0])) print("Computing time: {}".format(algorithm.total_computing_time))
if __name__ == "__main__": problem = Rastrigin(10) max_evaluations = 50000 algorithm = NSGAII( problem=problem, population_size=100, offspring_population_size=100, mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20.0), crossover=SBXCrossover(probability=0.9, distribution_index=20.0), termination_criterion=StoppingByEvaluations( max_evaluations=max_evaluations), dominance_comparator=DominanceComparator(), ) algorithm.run() front = algorithm.get_result() # Save results to file print_function_values_to_file( front, "FUN." + algorithm.get_name() + "-" + problem.get_name()) print_variables_to_file( front, "VAR." + algorithm.get_name() + "-" + problem.get_name()) print("Algorithm (continuous problem): " + algorithm.get_name()) print("Problem: " + problem.get_name()) print("Computing time: " + str(algorithm.total_computing_time))
] algorithm = SocioCognitiveEvolutionStrategy( problem=problem, strategies_params=strategies_params, termination_criterion=StoppingByEvaluations( max_evaluations=max_evaluations)) algorithm_normal = EvolutionStrategyWithHistory( problem=problem, mu=mu, lambda_=lambda_, elitist=elitist, mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables), termination_criterion=StoppingByEvaluations( max_evaluations=max_evaluations)) algorithm.run() result = algorithm.get_result() print('Algorithm: ' + algorithm.get_name()) print('Problem: ' + problem.get_name()) print('Solution: ' + str(result.variables[0])) print('Fitness: ' + str(result.objectives[0])) print('Computing time: ' + str(algorithm.total_computing_time)) print(result.variables) algorithm_normal.run() draw_comparision_plot(algorithm, algorithm_normal)
from jmetal.util.termination_criterion import StoppingByEvaluations if __name__ == "__main__": problem = Rastrigin(10) max_evaluations = 100000 algorithm = SimulatedAnnealing( problem=problem, mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20.0), termination_criterion=StoppingByEvaluations( max_evaluations=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: " + str(result.variables[0])) print("Fitness: " + str(result.objectives[0])) print("Computing time: " + str(algorithm.total_computing_time))