二元变量和约束的Pymoo优化问题

from pymoo.model.problem import Problem
from pymoo.model.problem import BinaryProblem
from pymoo.model.problem import Constraint
from pymoo.model.problem import BinaryVariable
from pymoo.algorithms.nsga2 import NSGA2
from pymoo.factory import get_termination
from pymoo.optimize import minimize

class MyProblem(BinaryProblem):
    def __init__(self):
        super().__init__(n_var=2, n_obj=1, n_constr=1, xl=0, xu=1)

    def _evaluate(self, x, out, *args, **kwargs):
        # 目标函数
        f1 = x[0] + x[1]

        # 约束条件
        g1 = x[0] - x[1]

        out["F"] = [f1]
        out["G"] = [g1]

problem = MyProblem()

algorithm = NSGA2(pop_size=100)

termination = get_termination("n_gen", 100)

res = minimize(problem,
               algorithm,
               termination,
               seed=1,
               verbose=True)

print("Best solution found: %s" % res.X)
print("Objective values: %s" % res.F)
print("Constraint violation: %s" % res.G)