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))
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
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()}")
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:')