xs = np.atleast_2d(xs)
return (-2.60 - 0.03 * xs[:, 0] - 0.02 * xs[:, 1] -
(0.01 / (1.39 - xs[:, 0]**2)) - (0.30 / (1.39 - xs[:, 1]**2)))
def f3(xs):
xs = np.atleast_2d(xs)
return -8.21 + (0.71 / (1.09 - xs[:, 0]**2))
def f4(xs):
xs = np.atleast_2d(xs)
return -0.96 + (0.96 / (1.09 - xs[:, 1]**2))
def objectives(xs):
return np.stack((f1(xs), f2(xs), f3(xs), f4(xs))).T
obj1 = _ScalarObjective("obj1", f1)
obj2 = _ScalarObjective("obj2", f2)
obj3 = _ScalarObjective("obj3", f3)
obj4 = _ScalarObjective("obj4", f4)
objkaikki = VectorObjective("obj", objectives)
# variables
var_names = ["x1", "x2"
] # Make sure that the variable names are meaningful to you.
initial_values = np.array([0.5, 0.5])
lower_bounds = [0.3, 0.3]
upper_bounds = [1.0, 1.0]
bounds = np.stack((lower_bounds, upper_bounds))
variables = variable_builder(var_names, initial_values, lower_bounds,
def f_3(x):
res = 8.21 - 0.71 / (1.09 - x[:, 0]**2)
return -res
def f_4(x):
res = 0.96 - 0.96 / (1.09 - x[:, 1]**2)
return -res
def f_5(x):
return np.max([np.abs(x[:, 0] - 0.65), np.abs(x[:, 1] - 0.65)], axis=0)
def c_1(x, f=None):
x = x.squeeze()
return (x[0] + x[1]) - 0.5
f1 = _ScalarObjective(name="f1", evaluator=f_1)
f2 = _ScalarObjective(name="f2", evaluator=f_2)
f3 = _ScalarObjective(name="f3", evaluator=f_3)
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],
)
c1 = ScalarConstraint("c1", 2, 5, evaluator=c_1)
problem = MOProblem(variables=varsl,
objectives=[f1, f2, f3, f4, f5],
constraints=[c1])
def f_4(x):
# min
res = -0.96 + 0.96 / (1.09 - x[:, 1]**2)
return res
# def f_5(x):
# return -0.96 + 0.96 / (1.09 - x[:, 1]**2)
def f_5(x):
# min
return np.max([np.abs(x[:, 0] - 0.65), np.abs(x[:, 1] - 0.65)], axis=0)
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)
def f_1(x):
# min x_1 + x_2 + x_3
res = x[:, 0] + x[:, 1] + x[:, 2]
return res
def f_2(x):
# max x_1 + x_2 + x_3
res = x[:, 0] + x[:, 1] + x[:, 2]
return res
def f_3(x):
# max x_1 + x_2 - x_3
res = -x[:, 0] - x[:, 1] - x[:, 2]
return res
f1 = _ScalarObjective(name="Price", evaluator=f_1, maximize=False)
f2 = _ScalarObjective(name="Quality", evaluator=f_2, maximize=True)
f3 = _ScalarObjective(name="Size", evaluator=f_3, maximize=True)
objl = [f1, f2, f3]
# define the variables, bounds -5 <= x_1 and x_2 <= 5
varsl = variable_builder(["x_1", "x_2", "x_3"],
initial_values=[0.5, 0.5, 0.5],
lower_bounds=[-5, -5, -5],
upper_bounds=[5, 5, 5])
# define constraints
def c_1(x, f=None):
x = x.squeeze()
# x_1 < 2
-10 *
np.exp(-0.2 * np.sqrt(xs[:, :-1]**2 + xs_plusone[:, :-1]**2)),
axis=1)
def f_2(xs: np.ndarray):
xs = np.atleast_2d(xs)
return np.sum(np.abs(xs)**0.8 + 5 * np.sin(xs**3), axis=1)
varsl = variable_builder(
["x_1", "x_2", "x_3"],
initial_values=[0, 0, 0],
lower_bounds=[-5, -5, -5],
upper_bounds=[5, 5, 5],
)
f1 = _ScalarObjective(name="f1", evaluator=f_1)
f2 = _ScalarObjective(name="f2", evaluator=f_2)
#For AI_DM ranges are always defined as IDEAL, NADIR.
f1_range = [-20, -14]
f2_range = [-14, 0.5]
x1 = np.linspace(min(f1_range), max(f1_range), 1000)
x2 = np.linspace(min(f2_range), max(f2_range), 1000)
y = np.linspace(0, 0, 1000)
problem = MOProblem(variables=varsl,
objectives=[f1, f2],
ideal=np.array([-20, -12]),
nadir=np.array([-14, 0.5]))
from desdeo_mcdm.interactive.NIMBUS import NIMBUS