解决PyMoo多目标优化问题中reshape array错误

Exception: ('Problem Error: F can not be set, expected shape (100, 2) but provided (1, 2)', ValueError('cannot reshape array of size 2 into shape (100,2)'))

import numpy as np
from pymoo.core.problem import ElementwiseProblem
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.optimize import minimize
from pymoo.operators.sampling.rnd import IntegerRandomSampling
from pymoo.operators.crossover.sbx import SBX
from pymoo.operators.mutation.pm import PM
from pymoo.visualization.scatter import Scatter

# Custom 2-objective, 5-integer optimization problem
class MyProblem(ElementwiseProblem):  # Corrected class here
    def __init__(self):
        super().__init__(
            n_var=5,
            n_obj=2,
            n_ieq_constr=0,
            xl=np.array([0,0,0,0,0]),  # Lower bounds for variables
            xu=np.array([10,10,10,10,10]),  # Upper bounds for variables
            vtype=int
        )

    def _evaluate(self, x, out, *args, **kwargs):
        # Objective functions
        f1 = np.sum(x ** 2)  # Minimize the sum of squares
        f2 = np.sum((x - 5) ** 2)  # Minimize the sum of squared deviations from 5

        # Assign objectives to the output
        out["F"] = [f1, f2]

# Instantiate the custom problem
problem = MyProblem()

# NSGA-II algorithm setup
algorithm = NSGA2(
    pop_size=100,
    n_offsprings=50,
    sampling=IntegerRandomSampling(),
    crossover=SBX(prob=1.0, eta=3.0),
    mutation=PM(prob=1.0, eta=3.0)
)

# Optimize the problem using NSGA-II
res = minimize(
    problem, 
    algorithm, 
    termination=('n_gen', 100), 
    seed=1,
    save_history=True,
    verbose=True
)

# Visualize the Pareto front
plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, color="red", s=30, label="NSGA-II")
plot.show()