退火与自适应退火python代码
时间: 2023-11-12 17:31:25 浏览: 28
以下是一个简单的退火算法的 Python 代码示例:
```python
import random
import math
# 定义目标函数
def target_function(x, y):
return math.sin(x) * math.cos(y)
# 定义退火算法函数
def simulated_annealing(initial_state, temperature, cooling_rate):
current_state = initial_state
best_state = current_state
while temperature > 1:
# 生成新的状态
new_state = (current_state[0] + random.uniform(-1, 1), current_state[1] + random.uniform(-1, 1))
# 计算目标函数值的变化量
delta = target_function(new_state[0], new_state[1]) - target_function(current_state[0], current_state[1])
# 如果新状态更优,则接受新状态
if delta > 0:
current_state = new_state
# 更新最优状态
if target_function(current_state[0], current_state[1]) > target_function(best_state[0], best_state[1]):
best_state = current_state
# 否则,以一定概率接受新状态
else:
if random.random() < math.exp(delta / temperature):
current_state = new_state
# 降低温度
temperature *= cooling_rate
return best_state
# 测试代码
initial_state = (random.uniform(-math.pi, math.pi), random.uniform(-math.pi, math.pi))
temperature = 100
cooling_rate = 0.99
best_state = simulated_annealing(initial_state, temperature, cooling_rate)
print("最优解:", best_state)
print("目标函数值:", target_function(best_state[0], best_state[1]))
```
以下是一个简单的自适应退火算法的 Python 代码示例:
```python
import random
import math
# 定义目标函数
def target_function(x, y):
return math.sin(x) * math.cos(y)
# 定义自适应退火算法函数
def adaptive_simulated_annealing(initial_state, max_iter, t0, alpha, k):
current_state = initial_state
best_state = current_state
t = t0
for i in range(max_iter):
# 生成新的状态
new_state = (current_state[0] + random.uniform(-1, 1), current_state[1] + random.uniform(-1, 1))
# 计算目标函数值的变化量
delta = target_function(new_state[0], new_state[1]) - target_function(current_state[0], current_state[1])
# 如果新状态更优,则接受新状态
if delta > 0:
current_state = new_state
# 更新最优状态
if target_function(current_state[0], current_state[1]) > target_function(best_state[0], best_state[1]):
best_state = current_state
# 否则,以一定概率接受新状态
else:
if random.random() < math.exp(delta / t):
current_state = new_state
# 更新温度
t = t / (1 + alpha * math.log(1 + k*i))
return best_state
# 测试代码
initial_state = (random.uniform(-math.pi, math.pi), random.uniform(-math.pi, math.pi))
max_iter = 1000
t0 = 100
alpha = 0.1
k = 0.01
best_state = adaptive_simulated_annealing(initial_state, max_iter, t0, alpha, k)
print("最优解:", best_state)
print("目标函数值:", target_function(best_state[0], best_state[1]))
```
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