python模拟退火算法实例

好的，以下是一个简单的 Python 模拟退火算法实例：

import math
import random

def simulated_annealing(cost_function, initial_solution, temperature, cooling_rate):
    current_solution = initial_solution
    best_solution = current_solution
    while temperature > 1:
        neighbor = get_neighbor(current_solution)
        current_cost = cost_function(current_solution)
        neighbor_cost = cost_function(neighbor)
        if neighbor_cost < current_cost:
            current_solution = neighbor
            if neighbor_cost < cost_function(best_solution):
                best_solution = neighbor
        else:
            delta = neighbor_cost - current_cost
            acceptance_probability = math.exp(-delta / temperature)
            if random.random() < acceptance_probability:
                current_solution = neighbor
        temperature *= cooling_rate
    return best_solution
def get_neighbor(solution):
    # 生成邻居解
    pass

def cost_function(solution):
    # 计算解的成本
    pass

# 示例用法
initial_solution = [0, 0, 0, 0, 0]
temperature = 100
cooling_rate = 0.95
best_solution = simulated_annealing(cost_function, initial_solution, temperature, cooling_rate)
print(best_solution)

其中，`cost_function` 函数用于计算解的成本，`get_neighbor` 函数用于生成邻居解。在 `simulated_annealing` 函数中，我们使用当前温度和冷却速率来控制算法的迭代次数和搜索范围。