self.tab = tab.strip() # 求解最⼤值还是最⼩值的标签: 'min' - 最⼩值;'max' - 最⼤值 >>>>>>>>>>>>#self.solve() # 完成主体的求解过程
self.display() # 数据可视化展⽰
def solve(self):
temp = 'deal_' + self.tab # 采⽤反射⽅法提取对应的函数
if hasattr(self, temp):
deal = getattr(self, temp)
else:
exit('>>>tab标签传参有误:"min"|"max"<<<')
x1 = self.x_seed
T = self.T_max
while T >= self.T_min:
for i in range(self.iterMax):
f1 = self.func(x1)
delta_x = random.random() * 2 - 1
if x1 + delta_x >= self.interval[0] and x1 + delta_x <= self.interval[1]: # 将随机解束缚在给定状态空间内 x2 = x1 + delta_x
else:
x2 = x1 - delta_x
f2 = self.func(x2)
delta_f = f2 - f1
x1 = deal(x1, x2, delta_f, T)
T *= self.rate
反体制self.x_solu = x1 # 提取最终退⽕解
def func(self, x): # 状态产⽣函数 - 即待求解函数
value = np.sin(x**2) * (x**2 - 5*x)
审核员return value
def p_min(self, delta, T): # 计算最⼩值时,容忍解的状态迁移概率
probability = np.exp(-delta/T)
return probability
def p_max(self, delta, T):
probability = np.exp(delta/T) # 计算最⼤值时,容忍解的状态迁移概率
return probability
def deal_min(self, x1, x2, delta, T):
if delta < 0: # 更优解
return x2
else: # 容忍解
P = self.p_min(delta, T)
if P > random.random(): return x2
else: return x1
def deal_max(self, x1, x2, delta, T):
if delta > 0: # 更优解
return x2
else: # 容忍解
P = self.p_max(delta, T)
阿米什人
if P > random.random(): return x2
else: return x1
def display(self):
print('seed: {}\nsolution: {}'.format(self.x_seed, self.x_solu))
plt.figure(figsize=(6, 4))
x = np.linspace(self.interval[0], self.interval[1], 300)
y = self.func(x)刘士豪
plt.plot(x, y, 'g-', label='function')
plt.plot(self.x_seed, self.func(self.x_seed), 'bo', label='seed')
plt.plot(self.x_solu, self.func(self.x_solu), 'r*', label='solution')
plt.title('solution = {}'.format(self.x_solu))
plt.title('solution = {}'.format(self.x_solu)) plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.savefig('SA.png', dpi=500)
plt.show()
plt.close()
if __name__ == '__main__':
SA([-5, 5], 'max')
结果展⽰
参考⽂献
《matlab在数学建模中的应⽤》