def test_min_vs_loop_vs_infill():
problem = get_problem("zdt1")
n_gen = 30
algorithm = NSGA2(pop_size=100)
min_res = minimize(problem, algorithm, ('n_gen', n_gen), seed=1)
algorithm = NSGA2(pop_size=100)
algorithm.setup(problem, ('n_gen', n_gen), seed=1)
while algorithm.has_next():
algorithm.next()
algorithm.finalize()
loop_res = algorithm.result()
np.testing.assert_allclose(min_res.X, loop_res.X)
algorithm = NSGA2(pop_size=100)
algorithm.setup(problem, ('n_gen', n_gen), seed=1)
while algorithm.has_next():
infills = algorithm.infill()
Evaluator().eval(problem, infills)
algorithm.advance(infills=infills)
algorithm.finalize()
infill_res = algorithm.result()
np.testing.assert_allclose(min_res.X, infill_res.X)
def test_no_feasible_solution_found():
class MyProblem(Problem):
def __init__(self):
super().__init__(n_var=2,
n_obj=1,
n_constr=36,
xl=np.array([0, 0]),
xu=np.array([100, 100]))
def _evaluate(self, x, out, *args, **kwargs):
f1 = x[:, 0] + x[:, 1]
out["F"] = np.column_stack([f1])
out["G"] = np.ones(len(x))
res = minimize(MyProblem(), NSGA2(), ("n_gen", 10), seed=1)
assert res.X is None
assert res.F is None
assert res.G is None
res = minimize(MyProblem(),
NSGA2(), ("n_gen", 10),
seed=1,
verbose=True,
return_least_infeasible=True,
save_history=True)
assert res.CV[0] == 1.0
def test_mutation_bin(name):
mut = get_mutation(name)
method = NSGA2(pop_size=20,
crossover=get_crossover('bin_ux'),
mutation=mut)
minimize(get_problem("zdt5"), method, ("n_gen", 20))
assert True
def test_pymoo_nsgaii(self):
moo_algorithm = NSGA2(pop_size=40,
n_offsprings=10,
sampling=get_sampling("real_random"),
crossover=get_crossover("real_sbx",
prob=0.9,
eta=15),
mutation=get_mutation("real_pm", eta=20),
eliminate_duplicates=True)
problem = ProblemConstraint()
algorithm = Pymoo(problem)
algorithm.options['verbose_level'] = 0
algorithm.options['n_iterations'] = 40
algorithm.options['algorithm'] = moo_algorithm
algorithm.run()
f_1 = []
f_2 = []
for individual in problem.last_population():
f_1.append(individual.costs[0])
f_2.append(individual.costs[1])
# print(len(problem.individuals))
# for individual in problem.individuals:
# print(individual)
self.assertLess(min(f_1), 1.5)
self.assertGreater(max(f_1), 74)
self.assertLess(max(f_2), 1.5)
self.assertGreater(max(f_2), 0.75)
def test_nsga2(self):
moo_algorithm = NSGA2(pop_size=10,
n_offsprings=10,
sampling=get_sampling("real_random"),
crossover=get_crossover("real_sbx",
prob=0.9,
eta=15),
mutation=get_mutation("real_pm", eta=20),
eliminate_duplicates=True)
self.moo(moo_algorithm)
def test_same_seed_same_result():
problem = get_problem("zdt3")
algorithm = NSGA2(pop_size=100, eliminate_duplicates=True)
res1 = minimize(problem, algorithm, ('n_gen', 20), seed=1)
np.random.seed(200)
res2 = minimize(problem, algorithm, ('n_gen', 20), seed=1)
np.testing.assert_almost_equal(res1.X, res2.X)
np.testing.assert_almost_equal(res1.F, res2.F)
def test_no_pareto_front_given():
class ZDT1NoPF(ZDT):
def _evaluate(self, x, out, *args, **kwargs):
f1 = x[:, 0]
g = 1 + 9.0 / (self.n_var - 1) * np.sum(x[:, 1:], axis=1)
f2 = g * (1 - np.power((f1 / g), 0.5))
out["F"] = np.column_stack([f1, f2])
algorithm = NSGA2(pop_size=100, eliminate_duplicates=True)
minimize(ZDT1NoPF(), algorithm, ('n_gen', 20), seed=1, verbose=True)
assert True
def test_thread_pool():
class MyThreadedProblem(ElementwiseProblem):
def __init__(self):
super().__init__(n_var=2,
n_obj=1,
n_constr=0,
parallelization=("threads", 4),
xl=np.array([0, 0]),
xu=np.array([100, 100]))
def _evaluate(self, x, out, *args, **kwargs):
out["F"] = x[0] + x[1]
minimize(MyThreadedProblem(),
NSGA2(), ("n_gen", 10),
seed=1,
save_history=False)
def main():
# Define search algorithms
algorithms = list()
# 1: GA
algorithm = GA(
pop_size=config.POPULATION_SIZE,
sampling=SudoRandomInitialization(),
# crossover=AdaptiveSinglePointCrossover(prob=0.8),
crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(),
eliminate_duplicates=RefactoringSequenceDuplicateElimination()
)
algorithms.append(algorithm)
# 2: NSGA II
algorithm = NSGA2(pop_size=config.POPULATION_SIZE,
sampling=SudoRandomInitialization(),
# crossover=AdaptiveSinglePointCrossover(prob=0.8),
crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(),
eliminate_duplicates=RefactoringSequenceDuplicateElimination()
)
algorithms.append(algorithm)
# 3: NSGA III
# Todo: Ask for best practices in determining ref_dirs
ref_dirs = get_reference_directions("energy", 8, 90, seed=1)
algorithm = NSGA3(ref_dirs=ref_dirs,
pop_size=config.POPULATION_SIZE,
sampling=SudoRandomInitialization(),
# crossover=AdaptiveSinglePointCrossover(prob=0.8),
crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(),
eliminate_duplicates=RefactoringSequenceDuplicateElimination()
)
algorithms.append(algorithm)
# Define problems
problems = list()
problems.append(
ProblemSingleObjective(n_refactorings_lowerbound=config.LOWER_BAND, n_refactorings_upperbound=config.UPPER_BAND)
)
problems.append(
ProblemMultiObjective(n_refactorings_lowerbound=config.LOWER_BAND, n_refactorings_upperbound=config.UPPER_BAND)
)
problems.append(
ProblemManyObjective(n_refactorings_lowerbound=config.LOWER_BAND, n_refactorings_upperbound=config.UPPER_BAND)
)
# Do optimization for various problems with various algorithms
res = minimize(problem=problems[2],
algorithm=algorithms[2],
termination=('n_gen', config.MAX_ITERATIONS),
seed=1,
verbose=True)
logger.info("** FINISHED **")
logger.info("Best Individual:")
logger.info(res.X)
logger.info("Objective Values:")
logger.info(res.F)
logger.info("==================")
logger.info("Other Solutions:")
for ind in res.opt:
logger.info(ind.X)
logger.info(ind.F)
logger.info("==================")
logger.info(f"Start Time: {res.start_time}")
logger.info(f"End Time: {res.end_time}")
logger.info(f"Execution Time in Seconds: {res.exec_time}")
from pymoo.factory import get_problem
from pymoo.optimize import minimize
from pymoo.algorithms.moo.nsga2 import NSGA2
problem = get_problem("zdt2")
algorithm = NSGA2(pop_size=10)
res = minimize(problem,
algorithm, ('n_gen', 100),
verbose=True,
return_least_infeasible=False,
seed=1)
# all decision variables
print('\nall decision variables:')
print(res.pop.get("X"))
print('\n')
# all objective variables
print('\nall objective variables:')
print(res.pop.get("F"))
print('\n')
# non-dominated set
print('\nnon-dominated set - decision variables:')
print(res.X)
print('\n')
print('\nnon-dominated set - objective variables:')
print(res.F)
def test_crossover_bin(name):
crossover = get_crossover(name, prob=0.95)
method = NSGA2(pop_size=20, crossover=crossover)
minimize(get_problem("zdt5"), method, ("n_gen", 20))
assert True
from pymoo.factory import get_problem, ZDT1, ZDT2, ZDT3
from pymoo.util.termination.max_eval import MaximumFunctionCallTermination
if __name__ == "__main__":
FOLDER = "/Users/blankjul/moo_benchmark"
recorder = None
recorder = DefaultMultiObjectiveRecorder()
benchmark = Benchmark(n_runs=11, recorder=recorder)
benchmark.add_problem("zdt1", ZDT1(), termination=("n_gen", 200))
# benchmark.add_problem("zdt2", ZDT2(), termination=("n_gen", 200))
# benchmark.add_problem("zdt3", ZDT3(), termination=("n_gen", 200))
benchmark.add_algorithm("nsga2", NSGA2())
benchmark.add_algorithm("gde3", GDE3())
loader = DefaultLoader(FOLDER)
writer = DefaultWriter(FOLDER)
results = benchmark.run(writer=writer,
loader=loader,
run_if_loading_fails=True)
# set the igd values for each of the problems
MultiObjectiveAnalyzer().run(results, benchmark=benchmark, inplace=True)
# now aggregate all the runs to have some representative values
attrs = [("igd", np.array, "igd"), ("igd", np.mean, "avg"),
("igd", np.std, "std")]
def main():
"""
Optimization module main driver
"""
# Define initialization objects
initializer_class = SmellInitialization if config.WARM_START else RandomInitialization
initializer_object = initializer_class(
udb_path=config.UDB_PATH,
population_size=config.POPULATION_SIZE,
lower_band=config.LOWER_BAND,
upper_band=config.UPPER_BAND
)
# -------------------------------------------
# Define optimization problems
problems = list() # 0: Genetic (Single), 1: NSGA-II (Multi), 2: NSGA-III (Many) objectives problems
problems.append(
ProblemSingleObjective(
n_objectives=config.NUMBER_OBJECTIVES,
n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND,
evaluate_in_parallel=False,
)
)
problems.append(
ProblemMultiObjective(
n_objectives=config.NUMBER_OBJECTIVES,
n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND,
evaluate_in_parallel=False,
)
)
problems.append(
ProblemManyObjective(
n_objectives=config.NUMBER_OBJECTIVES,
n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND,
evaluate_in_parallel=False,
verbose_design_metrics=True,
)
)
# Define search algorithms
algorithms = list()
# 1: GA
alg1 = GA(
pop_size=config.POPULATION_SIZE,
sampling=PopulationInitialization(initializer_object),
crossover=AdaptiveSinglePointCrossover(prob=config.CROSSOVER_PROBABILITY),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=config.MUTATION_PROBABILITY, initializer=initializer_object),
eliminate_duplicates=ElementwiseDuplicateElimination(cmp_func=is_equal_2_refactorings_list),
n_gen=config.NGEN,
)
algorithms.append(alg1)
# 2: NSGA-II
alg2 = NSGA2(
pop_size=config.POPULATION_SIZE,
sampling=PopulationInitialization(initializer_object),
crossover=AdaptiveSinglePointCrossover(prob=config.CROSSOVER_PROBABILITY),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=config.MUTATION_PROBABILITY, initializer=initializer_object),
eliminate_duplicates=ElementwiseDuplicateElimination(cmp_func=is_equal_2_refactorings_list),
n_gen=config.NGEN,
)
algorithms.append(alg2)
# 3: NSGA-III
# pop_size must be equal or larger than the number of reference directions
number_of_references_points = config.POPULATION_SIZE - int(config.POPULATION_SIZE * 0.20)
ref_dirs = get_reference_directions(
'energy', # algorithm
config.NUMBER_OBJECTIVES, # number of objectives
number_of_references_points, # number of reference directions
seed=1
)
alg3 = NSGA3(
ref_dirs=ref_dirs,
pop_size=config.POPULATION_SIZE, # 200
sampling=PopulationInitialization(initializer_object),
selection=TournamentSelection(func_comp=binary_tournament),
crossover=AdaptiveSinglePointCrossover(prob=config.CROSSOVER_PROBABILITY, ),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=config.MUTATION_PROBABILITY, initializer=initializer_object),
eliminate_duplicates=ElementwiseDuplicateElimination(cmp_func=is_equal_2_refactorings_list),
n_gen=config.NGEN,
)
algorithms.append(alg3)
# Termination of algorithms
my_termination = MultiObjectiveDefaultTermination(
x_tol=None,
cv_tol=None,
f_tol=0.0015,
nth_gen=5,
n_last=5,
n_max_gen=config.MAX_ITERATIONS, # about 1000 - 1400
n_max_evals=1e6
)
# Do optimization for various problems with various algorithms
res = minimize(
problem=problems[config.PROBLEM],
algorithm=algorithms[config.PROBLEM],
termination=my_termination,
seed=1,
verbose=False,
copy_algorithm=True,
copy_termination=True,
save_history=False,
callback=LogCallback(),
)
# np.save('checkpoint', res.algorithm)
# Log results
logger.info(f"***** Algorithm was finished in {res.algorithm.n_gen + config.NGEN} generations *****")
logger.info(" ")
logger.info("============ time information ============")
logger.info(f"Start time: {datetime.fromtimestamp(res.start_time).strftime('%Y-%m-%d %H:%M:%S')}")
logger.info(f"End time: {datetime.fromtimestamp(res.end_time).strftime('%Y-%m-%d %H:%M:%S')}")
logger.info(f"Execution time in seconds: {res.exec_time}")
logger.info(f"Execution time in minutes: {res.exec_time / 60}")
logger.info(f"Execution time in hours: {res.exec_time / (60 * 60)}")
# logger.info(f"Number of generations: {res.algorithm.n_gen}")
# logger.info(f"Number of generations", res.algorithm.termination)
# Log optimum solutions
logger.info("============ All opt solutions ============")
for i, ind in enumerate(res.opt):
logger.info(f'Opt refactoring sequence {i}:')
logger.info(ind.X)
logger.info(f'Opt refactoring sequence corresponding objectives vector {i}:')
logger.info(ind.F)
logger.info("-" * 75)
# Log best refactorings
logger.info("============ Best refactoring sequences (a set of non-dominated solutions) ============")
for i, ind in enumerate(res.X):
logger.info(f'Best refactoring sequence {i}:')
logger.info(ind)
logger.info("-" * 75)
logger.info("============ Best objective values (a set of non-dominated solutions) ============")
for i, ind_objective in enumerate(res.F):
logger.info(f'Best refactoring sequence corresponding objectives vector {i}:')
logger.info(ind_objective)
logger.info("-" * 75)
# Save best refactorings
population_trimmed = []
objective_values_content = ''
for chromosome in res.X:
chromosome_new = []
if config.PROBLEM == 0: # i.e., single objective problem
for gene_ in chromosome:
chromosome_new.append((gene_.name, gene_.params))
else:
for gene_ in chromosome[0]:
chromosome_new.append((gene_.name, gene_.params))
population_trimmed.append(chromosome_new)
for objective_vector in res.F:
objective_values_content += f'{res.algorithm.n_gen + config.NGEN},'
if config.PROBLEM == 0:
objective_values_content += f'{objective_vector},'
else:
for objective_ in objective_vector:
objective_values_content += f'{objective_},'
objective_values_content += '\n'
best_refactoring_sequences_path = os.path.join(
config.PROJECT_LOG_DIR,
f'best_refactoring_sequences_after_{res.algorithm.n_gen + config.NGEN}gens.json'
)
with open(best_refactoring_sequences_path, mode='w', encoding='utf-8') as fp:
json.dump(population_trimmed, fp, indent=4)
best_refactoring_sequences_objectives_path = os.path.join(
config.PROJECT_LOG_DIR,
f'best_refactoring_sequences_objectives_after_{res.algorithm.n_gen + config.NGEN}gens.csv'
)
with open(best_refactoring_sequences_objectives_path, mode='w', encoding='utf-8') as fp:
fp.write(objective_values_content)
try:
pf = res.F
# dm = HighTradeoffPoints()
dm = get_decision_making("high-tradeoff")
I = dm.do(pf)
logger.info("============ High-tradeoff points refactoring sequences ============")
for i, ind in enumerate(res.X[I]):
logger.info(f'High tradeoff points refactoring sequence {i}:')
logger.info(ind)
logger.info("-" * 75)
logger.info("============ High-tradeoff points objective values ============")
for i, ind_objective in enumerate(pf[I]):
logger.info(f'High-tradeoff points refactoring sequence corresponding objectives vector {i}:')
logger.info(ind_objective)
logger.info("-" * 75)
logger.info("High-tradeoff points mean:")
logger.info(np.mean(pf[I], axis=0))
logger.info("High-tradeoff points median:")
logger.info(np.median(pf[I], axis=0))
# Save high-tradeoff refactorings
population_trimmed = []
objective_values_content = ''
for chromosome in res.X[I]:
chromosome_new = []
if config.PROBLEM == 0: # i.e., single objective problem
for gene_ in chromosome:
chromosome_new.append((gene_.name, gene_.params))
else:
for gene_ in chromosome[0]:
chromosome_new.append((gene_.name, gene_.params))
population_trimmed.append(chromosome_new)
for objective_vector in pf[I]:
objective_values_content += f'{res.algorithm.n_gen + config.NGEN},'
if config.PROBLEM == 0:
objective_values_content += f'{objective_vector},'
else:
for objective_ in objective_vector:
objective_values_content += f'{objective_},'
objective_values_content += '\n'
high_tradeoff_path = os.path.join(
config.PROJECT_LOG_DIR,
f'high_tradeoff_points_refactoring_after_{res.algorithm.n_gen + config.NGEN}gens.json'
)
with open(high_tradeoff_path, mode='w', encoding='utf-8') as fp:
json.dump(population_trimmed, fp, indent=4)
high_tradeoff_path_objectives_path = os.path.join(
config.PROJECT_LOG_DIR,
f'high_tradeoff_points_after_{res.algorithm.n_gen + config.NGEN}gens.csv'
)
with open(high_tradeoff_path_objectives_path, mode='w', encoding='utf-8') as fp:
fp.write(objective_values_content)
except:
logger.error("No multi-optimal solutions (error in computing high tradeoff points)!")
def main():
# Define search algorithms
algorithms = list()
# 1: GA
algorithm = GA(
pop_size=config.POPULATION_SIZE,
sampling=PureRandomInitialization(),
crossover=AdaptiveSinglePointCrossover(prob=0.9),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=0.1),
eliminate_duplicates=ElementwiseDuplicateElimination(
cmp_func=is_equal_2_refactorings_list))
algorithms.append(algorithm)
# 2: NSGA II
algorithm = NSGA2(
pop_size=config.POPULATION_SIZE,
sampling=PureRandomInitialization(),
crossover=AdaptiveSinglePointCrossover(prob=0.9),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=0.1),
eliminate_duplicates=ElementwiseDuplicateElimination(
cmp_func=is_equal_2_refactorings_list))
algorithms.append(algorithm)
# 3: NSGA III
# pop_size must be equal or larger than the number of reference directions
number_of_references_points = config.POPULATION_SIZE - int(
config.POPULATION_SIZE * 0.20)
ref_dirs = get_reference_directions(
'energy', # algorithm
8, # number of objectives
number_of_references_points, # number of reference directions
seed=1)
algorithm = NSGA3(
ref_dirs=ref_dirs,
pop_size=config.POPULATION_SIZE, # 200
sampling=PureRandomInitialization(),
selection=TournamentSelection(func_comp=binary_tournament),
crossover=AdaptiveSinglePointCrossover(prob=0.8),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=0.1),
eliminate_duplicates=ElementwiseDuplicateElimination(
cmp_func=is_equal_2_refactorings_list))
algorithms.append(algorithm)
# -------------------------------------------
# Define problems
problems = list()
problems.append(
ProblemSingleObjective(n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND))
problems.append(
ProblemMultiObjective(n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND))
problems.append(
ProblemManyObjective(n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND,
evaluate_in_parallel=True))
# Termination of algorithms
my_termination = MultiObjectiveDefaultTermination(
x_tol=None,
cv_tol=None,
f_tol=0.0015,
nth_gen=10,
n_last=20,
n_max_gen=config.MAX_ITERATIONS, # about 1000 - 1400
n_max_evals=1e6)
# Do optimization for various problems with various algorithms
res = minimize(
problem=problems[2],
algorithm=algorithms[2],
termination=my_termination,
seed=1,
verbose=False,
copy_algorithm=True,
copy_termination=True,
save_history=False,
)
# np.save('checkpoint', res.algorithm)
# Log results
logger.info("\n** FINISHED **\n")
logger.info(
"Best refactoring sequences (a set of non-dominated solutions):")
logger.info(res.X)
logger.info("Best objective values (a set of non-dominated solutions):")
logger.info(res.F)
logger.info("=" * 75)
logger.info("Other solutions:")
for ind in res.opt:
logger.info(ind.X)
logger.info(ind.F)
logger.info("-" * 50)
logger.info("=" * 75)
logger.info(f"Start time: {res.start_time}")
logger.info(f"End time: {res.end_time}")
logger.info(f"Execution time in seconds: {res.exec_time}")
logger.info(f"Execution time in minutes: {res.exec_time / 60}")
logger.info(f"Execution time in hours: {res.exec_time / (60 * 60)}")
logger.info(f"Number of generations: {res.algorithm.n_gen}")
# logger.info(f"Number of generations", res.algorithm.termination)
pf = res.F
# dm = HighTradeoffPoints()
dm = get_decision_making("high-tradeoff")
try:
I = dm.do(pf)
logger.info(f"High tradeoff points: {pf[I][0]}")
logger.info(
f"High tradeoff points corresponding refactorings: {res.X[I]}")
logger.info(
f"The mean improvement of quality attributes: {np.mean(pf[I][0], axis=0)}"
)
logger.info(
f"The median improvement of quality attributes: {np.median(pf[I][0], axis=0)}"
)
except:
logger.info(
"No multi optimal solutions (error in computing high tradeoff points)!"
)
def search(self, data: Data, models: Collection[Model], tid: int,
**kwargs) -> np.ndarray:
kwargs = kwargs['kwargs']
# print ("SEARCH!")
prob = SurrogateProblem(self.problem, self.computer, data, models,
self.options, tid, self.models_transfer)
prob_pymoo = MyProblemPyMoo(self.problem.DP, self.problem.DO, prob)
if (kwargs['verbose']):
print("prob: ", prob)
bestX = []
if (self.problem.DO == 1): # single objective optimizer
if ('ga' == kwargs['search_algo']):
from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.optimize import minimize
algo = GA(pop_size=kwargs["search_pop_size"])
elif ('pso' == kwargs['search_algo']):
from pymoo.algorithms.soo.nonconvex.pso import PSO
from pymoo.optimize import minimize
algo = PSO(pop_size=kwargs["search_pop_size"])
else:
raise Exception(
f'Unknown optimization algorithm "{kwargs["search_algo"]}"'
)
bestX = []
res = minimize(prob_pymoo, algo, verbose=kwargs['verbose'], seed=1)
bestX.append(np.array(res.X).reshape(1, self.problem.DP))
else: # multi objective
if ('nsga2' == kwargs['search_algo']):
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.optimize import minimize
algo = NSGA2(pop_size=kwargs["search_pop_size"])
elif ('moead' == kwargs['search_algo']):
from pymoo.algorithms.moo.moead import MOEAD
from pymoo.optimize import minimize
from pymoo.factory import get_reference_directions
ref_dirs = get_reference_directions("das-dennis",
self.problem.DO,
n_partitions=12)
algo = MOEAD(ref_dirs,
n_neighbors=15,
prob_neighbor_mating=0.7)
else:
raise Exception(
f'Unknown optimization algorithm "{kwargs["search_algo"]}"'
)
bestX = []
res = minimize(prob_pymoo,
algo, ("n_gen", kwargs["search_gen"]),
verbose=kwargs['verbose'],
seed=1)
firstn = min(int(kwargs['search_more_samples']),
np.shape(res.X)[0])
xss = res.X[0:firstn]
bestX.append(xss)
if (kwargs['verbose']):
print(tid, 'OK' if cond else 'KO')
sys.stdout.flush()
print("bestX", bestX)
return (tid, bestX)
# any input reference direction function
pass
except Exception as e:
print(e)
sys.exit()
if alg_name == "NSGA2":
sampling_func = MOO_CONFIG["sampling_func"]
pop_size = alg_specific_args["pop_size"]
#################
# set algorithm #
#################
algorithm = NSGA2(
pop_size=pop_size,
sampling=get_sampling(sampling_func),
crossover=get_crossover(crossover_func, **crossover_func_args),
mutation=get_mutation(mutation_func, **mutation_func_args),
eliminate_duplicates=True
)
#####################
# algorithm logging #
#####################
MOO_log(
msg="algorithm = {}(\n"
"pop_size={},\n"
"sampling=get_sampling({}),\n"
"crossover=get_crossover({},{}),\n"
"mutation=get_mutation({},{}),\n"
"eliminate_duplicates=True\n"
")".format(
alg_name,
ES()
]
@pytest.mark.parametrize('problem', SINGLE_OBJECTIVE_PROBLEMS)
@pytest.mark.parametrize('algorithm', SINGLE_OBJECTIVE_ALGORITHMS)
def test_singe_obj(problem, algorithm):
res = minimize(problem, algorithm, seed=1, verbose=True)
fmin = problem.pareto_front().flatten()[0]
np.testing.assert_almost_equal(fmin, res.F[0], decimal=3)
MULTI_OBJECTIVE_PROBLEMS = [ZDT1()]
ref_dirs = get_reference_directions("das-dennis", 2, n_partitions=99)
MULTI_OBJECTIVE_ALGORITHMS = [
NSGA2(),
RVEA(ref_dirs),
MOEAD(ref_dirs),
ParallelMOEAD(ref_dirs),
AGEMOEA()
]
@pytest.mark.parametrize('problem', MULTI_OBJECTIVE_PROBLEMS)
@pytest.mark.parametrize('algorithm', MULTI_OBJECTIVE_ALGORITHMS)
def test_multi_obj(problem, algorithm):
res = minimize(problem, algorithm, ('n_gen', 300), seed=1, verbose=True)
pf = problem.pareto_front()
assert IGD(pf).do(res.F) < 0.05
