Python NSGAII Examples

Example #1

def optimal_panels_battery_multi(Ce, Cv, Cp, Cb, Vp, Vb, demand, irradiance,
                                 xp_min):

    energy_building = EnergyBuildingMulti(irradiance, demand, N, Ce, Cv, qinit,
                                          Cb, Vb, Cp, Vp, xp_min)
    algorithm = NSGAII(
        problem=energy_building,
        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_evaluations=25000))

    algorithm.run()
    solutions = algorithm.get_result()

    for solution in solutions:
        #print('Solution:',solution.variables, 'Maximum value:',solution.objectives[0])
        Xp = solution.variables[0]
        Xb = solution.variables[1]
        C = solution.objectives[0]
    print(f'Xp {Xp}, Xb {Xb}, Cost {C}')
    return Xb, Xp, C, algorithm

Example #2

def psp_test():
    path = '/home/thinkpad/Documents/jemoa/src/main/resources/instances/psp/o4p25_0.txt'
    psp_instance = PspInstance()
    psp_instance.read_(path)
    max_evaluations = 50000
    binary_string_length = psp_instance.n_var
    problem = PortfolioSocialProblem(psp_instance)

    algorithm = NSGAII(
        problem=problem,
        population_size=100,
        offspring_population_size=100,
        mutation=BitFlipMutation(probability=1.0 / binary_string_length),
        crossover=SPXCrossover(probability=1.0),
        termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
        dominance_comparator=DominanceComparator()
    )
    progress_bar = ProgressBarObserver(max=max_evaluations)
    algorithm.observable.register(progress_bar)

    algorithm.run()
    front = algorithm.get_result()
    for s in front:
        s.objectives = [-1 * obj for obj in s.objectives]
    # Save results to file
    # print_function_values_to_file(front, 'FUN.' + algorithm.get_name() + "-" + problem.get_name())
    print_solutions_to_file(front, DIRECTORY_RESULTS + 'SOLUTIONS.' + 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))

Example #3

File: nsgaii_solving_constrained_srinivas_problem.py Project: jMetal/jMetalPy

    read_solutions,
)
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_evaluations=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)

    print(f"Algorithm: {algorithm.get_name()}")
    print(f"Problem: {problem.get_name()}")

Example #4

File: ZDT NSGAI Test.py Project: jjsoper/CEQUAL-Opt

from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

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_evaluations=25000))

algorithm.run()

Example #5

File: main_llvm_multiobjective.py Project: hamena/iaas-llvm-ga-obfuscation

if __name__ == '__main__':

    # Problem set
    problem = llvmMultiobjetiveProblem(
        max_epochs=config_max_epochs,
        population_size=config_population_size,
        offspring_population_size=config_offspring_population_size,
        solution_length=config_solution_length,
        verbose=config_verbose)

    # Algorithm set
    algorithm = NSGAII(
        problem=problem,
        population_size=config_population_size,
        offspring_population_size=config_offspring_population_size,
        mutation=IntegerPolynomialMutation(config_probability_mutation),
        crossover=SBXCrossover(config_probability_crossover),
        selection=RandomSolutionSelection(),
        termination_criterion=problem)

    algorithm.run()

    nds = get_non_dominated_solutions(algorithm.get_result())

    with open(f"{problem.config_to_str()}_results.csv", "w") as file:
        for sol in nds:
            file.write(f'{sol.variables};{sol.objectives}\n')

    with open(f"{problem.config_to_str()}_results.data", "w") as file:
        #Outputs
        file.write('\nSettings:')