在 python 中使用 pyomo 绘制帕累托前沿时出错

问：

我正在尝试为我的多目标优化问题的简化版本绘制帕累托前沿（试图弄清楚一切是如何工作的）。我对此完全陌生，无法弄清楚我得到的错误意味着什么：

TypeError: '>' not supported between instances of 'generator' and 'int'

我正在使用现有代码进行绘图，所以我很确定错误出在我如何表述问题上，但我不确定它是什么。这是我的代码：

v=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
w=[22.0, 31.0, 0.0, 0.0, 11416.0, 0.0, 0.0, 0.0, 0.0, 15376.6, 977.97, 4324.97, 3264.79, 32.4, 43.02, 0.029, 0.2,0.00185, 0.00185, 0.0001, 0.03, 0.017, 0.0,0,0,0,0,0,0]
e=[562.51, 562.51, 0.0, 0.0, 223.16, 0.0, 0.0, 0.0, 0.0, 1401.63, 411.42, 1401.63, 0.0,312.53, 17195.71, 0.623, 15.14,0.01, 4.5, 23.42, 0.66,0,0,0,0,0,0,0,0]
g=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 840.000,469.000,46.000,18.000,4.000,12.000,0,0,0,0]
        
class WEN(Problem):

    def __init__(self):
        super().__init__(n_var=v, n_obj=2, n_ieq_constr=2, xl=np.array(0 for i in range(29)), xu=None)

    def _evaluate(self, x, out, *args, **kwargs):
        f1=[numpy.dot(v,e)]
        f2=[numpy.dot(v,w)]
        g1=[(numpy.dot(v*w)/100)-585]
        g2=[(numpy.dot(v*g)*0.00001)-1610]
        out["F"] = [f1, f2]
        out["G"] = [g1, g2]
        
from pymoo.visualization.scatter import Scatter
from pymoo.algorithms.moo.nsga2 import RankAndCrowdingSurvival
from pymoo.core.mixed import MixedVariableGA
from pymoo.optimize import minimize

problem = WEN()

algorithm = MixedVariableGA(pop_size=20, survival=RankAndCrowdingSurvival())

res = minimize(problem,
               algorithm,
               ('n_gen', 50),
               seed=1,
               verbose=False)

plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, facecolor="none", edgecolor="red")
plot.show()        

任何帮助、提示、建议（关于 Python 编码和/或 Python 优化的任何方面）将不胜感激。