def test_against_orginal_implementation(self):
for problem in [
Ackley(n_var=2),
Rosenbrock(n_var=2),
Sphere(n_var=10),
]:
print(problem.__class__.__name__)
x0 = FloatRandomSampling().do(problem, 1)[0].X
problem.xl = None
problem.xu = None
rho = 0.5
pop = run(problem, x0, rho=rho)[1:]
delta = np.zeros(problem.n_var)
for i in range(0, problem.n_var):
if (x0[i] == 0.0):
delta[i] = rho
else:
delta[i] = rho * abs(x0[i])
algorithm = PatternSearch(x0=x0, explr_delta=delta)
ret = minimize(problem, algorithm, verbose=True)
X, _X = pop.get("X"), ret.pop.get("X")
F, _F = pop.get("F"), ret.pop.get("F")
n = min(len(X), len(_X))
X, _X, F, _F = X[:n], _X[:n], F[:n], _F[:n]
def test_sphere_with_constraints(self):
problem = SphereWithConstraints()
for algorithm in [GA(), NelderMead(), PatternSearch(), CuckooSearch()]:
f, f_opt = test(problem, algorithm)
self.assertAlmostEqual(f, f_opt, places=3)
print(problem.__class__.__name__, algorithm.__class__.__name__,
"Yes")
def test_sphere(self):
problem = Sphere()
for algorithm in [NelderMead(), PatternSearch(), PSO(), GA()]:
f, f_opt = test(problem, algorithm)
self.assertAlmostEqual(f, f_opt, places=5)
print(problem.__class__.__name__, algorithm.__class__.__name__,
"Yes")
def test_sphere_no_bounds(self):
problem = SphereNoBounds()
x0 = np.random.random(problem.n_var)
for algorithm in [NelderMead(x0=x0), PatternSearch(x0=x0)]:
f, f_opt = test(problem, algorithm)
self.assertAlmostEqual(f, f_opt, places=5)
print(problem.__class__.__name__, algorithm.__class__.__name__,
"Yes")
from pymoo.algorithms.so_pattern_search import PatternSearch
from pymoo.factory import get_problem
from pymoo.optimize import minimize
problem = get_problem("ackley", n_var=30)
algorithm = PatternSearch()
res = minimize(problem, algorithm, seed=1, verbose=False)
print("Best solution found: \nX = %s\nF = %s" % (res.X, res.F))
# elementwise_evaluation=True
)
def _evaluate(self, X, out, *args, **kwargs):
w, sigma_max = function(X)
sysMass = np.prod(X, axis=1) * MaterialDensity
out["F"] = np.column_stack([w, sysMass])
out["G"] = np.column_stack([-w, -sysMass])
problem = MyProblem()
algorithm = PatternSearch(explr_delta=0.1,
explr_rho=0.3,
pattern_step=2,
eps=1e-08)
from pymoo.util.termination.default import MultiObjectiveDefaultTermination
termination = MultiObjectiveDefaultTermination(x_tol=1e-8,
cv_tol=1e-6,
f_tol=0.0025,
nth_gen=5,
n_last=30,
n_max_gen=1000,
n_max_evals=100000)
from pymoo.optimize import minimize
res = minimize(problem,