print("optimizing for planet : {}".format(p_name))
print("radius : {}".format(radius))
print("density: {}".format(density))
print("escape velocity: {}".format(escape_velocity))
print("surface temperature : {}".format(surface_temperature))
print("")
problem = get_modified_crs_objective(radius, density, escape_velocity,
surface_temperature)
# problem = get_modified_drs_objective(radius, density, escape_velocity,surface_temperature)
algorithm = MOQPSO(problem=problem,
swarm_size=100,
max_evaluations=100000,
mutation=Polynomial(probability=0.3,
distribution_index=20),
leaders=CrowdingDistanceArchive(500),
reference_point=[0, 0])
# algorithm = SMPSO(
# problem=problem,
# swarm_size=100,
# max_evaluations=100000,
# mutation=Polynomial(probability=0.2, distribution_index=10),
# leaders=CrowdingDistanceArchive(100),
# reference_point=[0] * problem.number_of_objectives
# )
try:
algorithm.run()
front = algorithm.get_result()
from jmetal.algorithm import NSGAII
from jmetal.operator import Polynomial, SBX, BinaryTournamentSelection
from jmetal.component import RankingAndCrowdingDistanceComparator, BasicAlgorithmObserver
from jmetal.problem import ZDT1
algorithm = NSGAII(problem=ZDT1(),
population_size=100,
max_evaluations=25000,
mutation=Polynomial(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBX(probability=1.0, distribution_index=20),
selection=BinaryTournamentSelection(
comparator=RankingAndCrowdingDistanceComparator()))
algorithm.run()
front = algorithm.get_result()
print(front)