模拟退火算法insight代码

模拟退火算法是一种全局优化搜索方法，灵感来源于金属冷却过程中的相变现象。它通过随机搜索和局部最优解之间的"接受"机制来找到全局最优解。以下是一个简单的Python代码示例，使用了NumPy库来实现模拟退火算法：

import numpy as np

def energy(state, problem_func):
    # 问题函数，输入状态返回能量值
    return problem_func(state)

def random_neighbor(current_state, low, high):
    # 随机生成邻域状态
    new_state = current_state + (np.random.rand(len(current_state)) * (high - low))
    return np.clip(new_state, low, high)  # 确保新状态在可行范围内

def simulated_annealing(current_state, low, high, T_start, cooling_rate, problem_func, max_iter=1000):
    T = T_start
    best_energy = energy(current_state, problem_func)
    for _ in range(max_iter):
        new_state = random_neighbor(current_state, low, high)
        new_energy = energy(new_state, problem_func)

        if new_energy < best_energy or np.exp((best_energy - new_energy) / T) > np.random.uniform(0, 1):
            current_state = new_state
            best_energy = new_energy

        T *= cooling_rate
    return current_state, best_energy

# 使用例子：
low, high = -5, 5  # 假设这是我们的搜索空间范围
current_state = np.zeros_like(low)  # 初始状态
T_start = 100
cooling_rate = 0.99
solution, min_energy = simulated_annealing(current_state, low, high, T_start, cooling_rate, your_problem_function)
print(f"最小能量位置: {solution}, 最小能量: {min_energy}")