我正在使用此代码来解决多目标优化模型（电源调度）并尝试在我的代码中调整一个示例。
示例：https://stackoverflow.com/questions/50742999/multi-objective-optimization-example-pyomo。
我试图跳过“低效的帕累托前沿”部分并直接绘制“有效的帕累托前沿”。
第一个选项卡可以正常运行并生成Cost_min、Cost_max、Emission_min、Emission_max。
from pyomo.environ import *
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import random
# create a model
model = AbstractModel()
# declare decision variables
model.N = Param(mutable=True)
model.J = RangeSet(model.N)
model.A = Param(model.J)
model.B = Param(model.J)
model.C = Param(model.J)
model.D = Param(model.J)
model.E = Param(model.J)
model.F = Param(model.J)
model.P_min = Param(model.J, within=PositiveReals)
model.P_max = Param(model.J, within=PositiveReals)
model.demand = Param(mutable=True)
# declare constraints
def Pbounds(model, j):
return (model.P_min[j], model.P_max[j])
model.P = Var(model.J, bounds=Pbounds, domain=NonNegativeReals)
def P_LoadgenBalance(model):
return sum(model.P[j] for j in model.J) >= model.demand
model.P_LoadgenBalance = Constraint(rule=P_LoadgenBalance)
# declare objective_cost
def obj_cost(model):
return sum(model.A[j]* model.P[j] ** 2 + model.B[j] * model.P[j] + model.C[j] for j in model.J)
model.cost= Objective(rule=obj_cost, sense=minimize)
# declare objective_emission
def obj_emission(model):
return sum(model.E[j]* model.P[j] ** 2 + model.D[j] * model.P[j] + model.F[j] for j in model.J)
model.emission= Objective(rule=obj_emission, sense=minimize)
# deactivate model.emission calculate emission_max,cost_min
model.emission.deactivate()
instance = model.create_instance("E:\pycharm_project\PD\END-10units.dat")
opt = SolverFactory('Ipopt')
results = opt.solve(instance)
for i in instance.J:
print(i,value(instance.P[i]))
print( 'cost = ' + str(value(instance.cost)) )
print( 'emission = ' + str(value(instance.emission)) )
emission_max = value(instance.emission)
cost_min = value(instance.cost)
# ## max emission deactivate model.cost calculate emission_min,cost_max
model.emission.activate()
model.cost.deactivate()
instance = model.create_instance("E:\pycharm_project\PD\END-10units.dat")
results = opt.solve(instance)
for i in instance.J:
print(i,value(instance.P[i]))
print( 'cost = ' + str(value(instance.cost)) )
print( 'emission = ' + str(value(instance.emission)) )
emission_min = value(instance.emission)
cost_max = value(instance.cost)
运行此选项卡中的代码后，未生成任何错误。但是当输出一个帕累托前沿时，这里只显示一个点。
# ## apply normal $\epsilon$-Constraint
model.emission.deactivate()
model.cost.activate()
model.emission_value = Param(initialize=0, mutable=True)
def c_epsilon(model):
return model.emission <= model.emission_value
model.C_epsilon = Constraint(rule=c_epsilon)
results = opt.solve(instance)
print('Each iteration will keep emission lower than some values between emission_min and emission_max, so [' + str(emission_min) + ', ' + str(emission_max) + ']')
n = 5
step = int((emission_max - emission_min) / n)
steps = list(range(int(emission_min), int(emission_max), step)) + [emission_max]
# ## apply augmented $\epsilon$-Constraint
# max emission + delta*epsilon <br>
# s.t. emission - s = emission_value
model.del_component(model.cost)
model.del_component(model.emission)
model.del_component(model.C_epsilon)
model.delta = Param(initialize=0.00001)
model.s = Var(within=NonNegativeReals)
def obj_cost_1(model):
return sum(model.cost+model.delta * model.s)
model.obj_cost_1 = Objective(rule=obj_cost_1, sense=maximize)
def C_e(model):
return model.emission-model.s==model.emission_value
model.C_e= Constraint(rule=C_e)
cost_l = []
emission_l = []
for i in steps:
model.emission_value = i
results = opt.solve(instance)
cost_l.append(value(instance.cost))
emission_l.append(value(instance.emission))
plt.plot(cost_l,emission_l,'o-.');
plt.title('efficient Pareto-front');
plt.grid(True);
plt.show()
结果如下所示。不知道为什么这不能输出正确的帕累托图。不知道代码的哪一步错了。
高效的 Pareto-front 谁能帮我处理这段代码？谢谢。维维
