counter += 1
            # build problem
            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
            )
        return x[0] + 2

    # 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)