upper_bounds=prob.upper,
)
objective = VectorObjective(name=obj_names, evaluator=prob.eval)
problem = MOProblem([objective], variables, None)
problem.ideal = prob.ideal
problem.nadir = (
abs(np.random.normal(size=n_obj, scale=0.15)) + 1
) * prob.nadir
true_nadir = prob.nadir
scalar = asf(ideal=problem.ideal, nadir=true_nadir)
# a posteriori
a_post_rvea = RVEA(
problem=problem, interact=False, n_gen_per_iter=gen, n_iterations=4
)
a_post_nsga = NSGAIII(
problem=problem, interact=False, n_gen_per_iter=gen, n_iterations=4
)
# interactive
int_rvea = RVEA(problem=problem, interact=True, n_gen_per_iter=gen)
int_nsga = NSGAIII(problem=problem, interact=True, n_gen_per_iter=gen)
# New algorithm
nimb_rvea = IOPIS_RVEA(problem, n_gen_per_iter=gen)
nimb_nsga = IOPIS_NSGAIII(problem, n_gen_per_iter=gen)
responses = np.random.rand(4, n_obj)
_, pref_int_rvea = int_rvea.requests()
_, pref_int_nsga = int_nsga.requests()
x_names = [f"x{i}" for i in range(1, num_var[problem_name] + 1)]
y_names = ["f1", "f2"]
data = pd.DataFrame(np.hstack((x, y.objectives)),
columns=x_names + y_names)
problem = DataProblem(data=data,
variable_names=x_names,
objective_names=y_names)
problem.train(LipschitzianRegressor)
evolver_L_opt = oRVEA(problem, use_surrogates=True)
while evolver_L_opt.continue_evolution():
evolver_L_opt.iterate()
evolver_L = RVEA(problem, use_surrogates=True)
while evolver_L.continue_evolution():
evolver_L.iterate()
evolver_L_robust = robust_RVEA(problem, use_surrogates=True)
while evolver_L_robust.continue_evolution():
evolver_L_robust.iterate()
problem.train(GaussianProcessRegressor)
evolver_G_opt = oRVEA(problem, use_surrogates=True)
while evolver_G_opt.continue_evolution():
evolver_G_opt.iterate()
evolver_G = RVEA(problem, use_surrogates=True)
while evolver_G.continue_evolution():
evolver_G.iterate()
f1 = _ScalarObjective(name="f1", evaluator=f_1, maximize=[True])
f2 = _ScalarObjective(name="f2", evaluator=f_2, maximize=[True])
f3 = _ScalarObjective(name="f3", evaluator=f_3, maximize=[True])
f4 = _ScalarObjective(name="f4", evaluator=f_4)
f5 = _ScalarObjective(name="f5", evaluator=f_5)
varsl = variable_builder(
["x_1", "x_2"],
initial_values=[0.5, 0.5],
lower_bounds=[0.3, 0.3],
upper_bounds=[1.0, 1.0],
)
problem = MOProblem(variables=varsl, objectives=[f1, f2, f3, f4, f5])
evolver = RVEA(problem, interact=True, n_iterations=10, n_gen_per_iter=100)
_, pref = evolver.iterate()
n_iteration = 1
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("10.0.2.15", 5005))
# sock.bind(("127.0.0.1", 5005))
sock.listen(1)
print("Waiting for ComVis to connect...")
connection, client_addr = sock.accept()
print("Connection estabilished!")
while True:
for gen in num_gen_per_iter:
for n_obj, n_var in zip(n_objs, n_vars):
for problem_name in problem_names:
print(f"Loop {counter} of {total_count}")
counter += 1
problem = test_problem_builder(name=problem_name,
n_of_objectives=n_obj,
n_of_variables=n_var)
problem.ideal = np.asarray([0] * n_obj)
problem.nadir = abs(np.random.normal(size=n_obj, scale=0.15)) + 1
true_nadir = np.asarray([1] * n_obj)
# interactive
int_rvea = RVEA(problem=problem, interact=True, n_gen_per_iter=gen)
int_nsga = NSGAIII(problem=problem,
interact=True,
n_gen_per_iter=gen)
# initial reference point is specified randomly
response = np.random.rand(n_obj)
# run algorithms once with the randomly generated reference point
_, pref_int_rvea = int_rvea.requests()
_, pref_int_nsga = int_nsga.requests()
pref_int_rvea.response = pd.DataFrame(
[response],
columns=pref_int_rvea.content["dimensions_data"].columns)
pref_int_nsga.response = pd.DataFrame(
[response],
from desdeo_problem.testproblems.TestProblems import test_problem_builder
from desdeo_emo.EAs.RVEA import RVEA
from desdeo_emo.EAs.NSGAIII import NSGAIII
from desdeo_emo.othertools.plotlyanimate import animate_init_, animate_next_
dtlz3 = test_problem_builder("DTLZ3", n_of_variables=12, n_of_objectives=11)
evolver = RVEA(dtlz3, n_iterations=10)
figure = animate_init_(evolver.population.objectives, filename="dtlz3.html")
while evolver.continue_evolution():
evolver.iterate()
figure = animate_next_(
evolver.population.objectives,
figure,
filename="dtlz3.html",
generation=evolver._iteration_counter,
)
list_vars = variable_builder(["x", "y"],
initial_values=[0, 0],
lower_bounds=[0, 0],
upper_bounds=[np.pi, np.pi])
f1 = _ScalarObjective(name="f1", evaluator=f_1)
f2 = _ScalarObjective(name="f2", evaluator=f_2)
c1 = ScalarConstraint("c1", 2, 2, c_1)
c2 = ScalarConstraint("c2", 2, 2, c_2)
problem = MOProblem(variables=list_vars,
objectives=[f1, f2],
constraints=[c1, c2])
evolver = RVEA(problem)
"""
figure = animate_init_(evolver.population.objectives, filename="MOPC4.html")
"""
while evolver.continue_evolution():
evolver.iterate()
"""figure = animate_next_(
evolver.population.objectives,
figure,
filename="MOPC4.html",
generation=evolver._iteration_counter,
)
"""