Python解决非线性规划中经济调度问题

import numpy as np
from scipy.optimize import minimize
#目标函数
def fun(args1):
    a,b,c,d=args1
    r=lambda x:(a*x[0]*x[0]+b*x[1]*x[1]+c*x[2]*x[2]+d)
    return r
def con(args2):
    x0min,x1min,x2min=args2
    cons=({'type':'eq','fun':lambda x:-x[0]-x[1]**2+2},
          {'type':'eq','fun':lambda x:x[1]+2*x[2]**2-3},
          {'type':'ineq','fun':lambda x:x[0]**2-x[1]+x[2]**2},
          {'type': 'ineq', 'fun': lambda x: -(x[0]+x[1]**2+x[2]**2-20)},
          {'type':'ineq','fun':lambda x:x[0]-x0min},
          {'type':'ineq','fun':lambda x:x[1]-x1min},
          {'type':'ineq','fun':lambda x:x[2]+x2min})
    return cons
def main():
    args1=(1,2,3,8)
    args2=(0,0,0)
    cons=con(args2)
    x0=np.array((1,2,3))    #初值
    res=minimize(fun(args1),x0,method='SLSQP',constraints=cons)
    print('minf(x):',res.fun)
    print(res.success)
    print('x：',[np.around(i) for i in res.x])
    print('x1:',res.x[0])
    print('x2:',res.x[1])
    print('x3:',res.x[2])
    #另一种表述
    print("optimization problem(res):{}".fORMat(res.x))
    print("Xopt={}".format(res.x))
    print("minf(x)={:.4f}".format(res.fun))
 
if __name__ == "__main__":
    main()

#from sko.DE import DE   #差分进化法
from sko.GA import GA   #遗传算法
def obj_func(p):
    x1, x2, x3 = p
    return x1 ** 2 + 2*x2 ** 2 + 3*x3 ** 2+8
 
 
constraint_eq = [
    lambda x: -x[0]-x[1]**2+2,
    lambda x: x[1]+2*x[2]**2-3
]
 
constraint_ueq = [
    lambda x: -x[0]**2+x[1]-x[2]**2,
    lambda x: x[0]+x[1]**2+x[2]**2-20
]
 
#de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0],constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)
ga = GA(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0],
        constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)
 
 
#best_x, best_y = de.run()
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

import numpy as np
from scipy.optimize import minimize
#目标函数（FG1+FG2+FG3）
def fun(args1):
    a0,a1,a2,b0,b1,b2,c0,c1,c2=args1
    v=lambda x:(a0+a1*x[0]+a2*x[0]*x[0]
                +b0+b1*x[1]+b2*x[1]*x[1]
                +c0+c1*x[2]+c2*x[2]*x[2])
    return v
def con(args2):
    D,x0min,x0max,x1min,x1max,x2min,x2max=args2
    cons=({'type':'eq','fun':lambda x:D-x[0]-x[1]-x[2]},
          {'type':'ineq','fun':lambda x:x[0]-x0min},
          {'type':'ineq','fun':lambda x:-x[0]+x0max},
          {'type':'ineq','fun':lambda x:x[1]-x1min},
          {'type':'ineq','fun':lambda x:-x[1]+x1max},
          {'type':'ineq','fun':lambda x:x[2]-x2min},
          {'type':'ineq','fun':lambda x:-x[2]+x2max})
    return cons
def main():
    args1=(4,0.3,0.0007,3,0.32,0.0004,3.5,0.3,0.00045)
    args2=(700,100,200,120,250,150,300)
    cons=con(args2)
    x0=np.array((150,250,200))    #初值
    res=minimize(fun(args1),x0,method='SLSQP',constraints=cons)
    print('FGi-代价:',res.fun)
    print(res.success)
    print('PGi—解：',[np.around(i) for i in res.x])
    print('PG1:',res.x[0])
    print('PG2:',res.x[1])
    print('PG3:',res.x[2])
 
if __name__ == "__main__":
    main()

from pyswarm import pso
 
def object_func(x):
    return (4+0.3*x[0]+0.0007*x[0]*x[0]+3+0.32*x[1]+0.0004*x[1]*x[1]+3.5+0.3*x[2]+0.00045*x[2]*x[2])
 
#不等式约束
 
def cons1(x):
    return [x[0]+x[1]+x[2]-700]
 
lb = [100, 120, 150]#
ub = [200, 250, 300]
 
xopt, fopt = pso(object_func,lb,ub,ieqcons=[cons1], maxiter=100,swarmsize=1000)
print(xopt)
print(fopt)