Python退火算法在高次方程的应用

 import random
 import matplotlib.pyplot as plt
 list_x = []
 # for i in range(1):
 #   #print(random.randint(0,100))
 #   for i in range(0,100):
 #     print("sss",i)
 #
 #   list_x.append(random.randint(0,100))
 for i in range(-100,100):
   list_x.append(i/10)
 print("横坐标为：",list_x)
 print(len(list_x))
 list_y = []
 for x in list_x:
   # print(x)
   #y = x*x*x - 60*x*x -4*x +6
   y = (x - 2) * (x + 3) * (x + 8) * (x - 9)
   list_y.append(y)
 print("纵坐标为：",list_y)
 #经验证，这里算出来的结果6.5和最优解1549都是对的
 print("最小值为：",min(list_y))
 num = min(list_y)
 print("最优解：",list_y.index(num)/10)
 print("第",list_y.index(num)/10-10,"个位置取得最小值")
 plt.plot(list_x, list_y, label='NM')
 #plt.plot(x2, y2, label='Second Line')
 plt.xlabel('X') #横坐标标题
 plt.ylabel('Y') #纵坐标标题
 #plt.title('Interesting Graph\nCheck it out',loc="right")  #图像标题
 #plt.title('Interesting Graph\nCheck it out')
 plt.legend()  #显示Fisrt Line和Second Line（label）的设置
 plt.savefig('C:/Users/zhengyong/Desktop/1.png')
 plt.show()

 #自己改写的退火算法计算方程(x - 2) * (x + 3) * (x + 8) * (x - 9)的计算方法
 #class没啥用
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import pyplot as plt 
 #设置基本参数
 #T初始温度，T_stop，iter_num每个温度的迭代次数，Q温度衰减次数
 class Tuihuo_alg():
   def __init__(self,T_start,iter_num,T_stop,Q,xx,init_x):
     self.T_start = T_start
     self.iter =iter_num
     self.T_stop = T_stop
     self.Q = Q
     self.xx = xx
     self.init_x = init_x
   # def cal_x2y(self):
   #   return (x - 2) * (x + 3) * (x + 8) * (x - 9)
 if __name__ == '__main__':
   def cal_x2y(x):
     #print((x - 2) * (x + 3) * (x + 8) * (x - 9))
     return (x - 2) * (x + 3) * (x + 8) * (x - 9)
   T_start = 1000
   iter_num = 1000
   T_stop = 1
   Q = 0.95
   K = 1
   l_boundary = -10
   r_boundary = 10
   #初始值
   xx = np.linspace(l_boundary, r_boundary, 300)
   yy = cal_x2y(xx)
   init_x =10 * ( 2 * np.random.rand() - 1)
   print("init_x:",init_x)
   t = Tuihuo_alg(T_start,iter_num,T_stop,Q,xx,init_x)
   val_list = [init_x]
   while T_start>T_stop:
     for i in range(iter_num):
       init_y = cal_x2y(init_x)
       #这个区间(2 * np.random.rand() - 1)本身是（-1,1），所以加上就是一个随机加或者减过程
       new_x = init_x + (2 * np.random.rand() - 1)
       if l_boundary <= new_x <= r_boundary:
         new_y = cal_x2y(new_x)
     #print("new_x:",new_x)
     #print('new_y:',new_y)
         delta = new_y - init_y #新减旧
         if delta <0:
           init_x = new_x
         else:
           p = np.exp(-delta / (K * T_start))
           if np.random.rand()