var_names = ["x" + str(i + 1) for i in range(n_var)]
obj_names = ["f" + str(i + 1) for i in range(n_obj)]
prob = wfg_problem(
name=problem_name, n_of_objectives=n_obj, n_of_variables=n_var
)
variables = variable_builder(
names=var_names,
initial_values=prob.lower,
lower_bounds=prob.lower,
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)
# name of constraints, num variables, num objectives, evaluator
c1 = ScalarConstraint("c1", len(varsl), len(objl), evaluator=c_1)
problem = MOProblem(variables=varsl, objectives=objl, constraints=[c1])
max_bool = list(
map(lambda x: True if x < 0 else False, problem._max_multiplier))
max_multiplier = problem._max_multiplier
# define ideal and nadir
ideal = max_multiplier * np.array([-15, 15, 15])
nadir = max_multiplier * np.array([15, -15, -15])
problem.ideal = ideal
problem.nadir = nadir
# GLOBAL
global method
method = NIMBUS(problem, scalar_method="scipy_de")
ideal = method._ideal
nadir = method._nadir
# GLOBAL
global plotter
idealnadir = np.stack((ideal, nadir))
scaler = MinMaxScaler((-1, 1))
scaler.fit(idealnadir)
plotter = Plotter(method._nadir, method._ideal, scaler, max_bool)