模拟退火算法 给出代码

以下是一个简单的示例代码，演示了如何使用Python实现模拟退火算法：

import math
import random

def simulated_annealing(initial_solution, cost_function, max_iterations, initial_temperature, cooling_rate):
    current_solution = initial_solution
    best_solution = initial_solution
    
    current_cost = cost_function(current_solution)
    best_cost = current_cost
    
    temperature = initial_temperature
    
    for i in range(max_iterations):
        # Generate a new candidate solution
        new_solution = generate_neighbor(current_solution)
        new_cost = cost_function(new_solution)
        
        # Calculate acceptance probability
        acceptance_prob = acceptance_probability(current_cost, new_cost, temperature)
        
        # Accept or reject the new solution
        if acceptance_prob > random.random():
            current_solution = new_solution
            current_cost = new_cost
        
        # Update the best solution if necessary
        if new_cost < best_cost:
            best_solution = new_solution
            best_cost = new_cost
        
        # Cool down the temperature
        temperature *= cooling_rate
    
    return best_solution

def generate_neighbor(solution):
    # Generate a random neighbor solution based on the current solution
    # This can be done by making small modifications to the current solution
    # For example, swap two elements in a list-based solution
    
    # TODO: Implement your own logic here
    
    return neighbor_solution

def acceptance_probability(current_cost, new_cost, temperature):
    if new_cost < current_cost:
        return 1.0
    
    delta_cost = new_cost - current_cost
    return math.exp(-delta_cost / temperature)

# Example usage

# Define your cost function
def cost_function(solution):
    # TODO: Implement your own cost function here
    # The cost function evaluates the quality of a solution
    # Lower cost values indicate better solutions
    
    return cost_value

# Define your initial solution
initial_solution = ...

# Set the maximum number of iterations
max_iterations = ...

# Set the initial temperature and cooling rate
initial_temperature = ...
cooling_rate = ...

# Run the simulated annealing algorithm
best_solution = simulated_annealing(initial_solution, cost_function, max_iterations, initial_temperature, cooling_rate)

# Print the best solution and its cost
print("Best Solution:", best_solution)
print("Best Cost:", cost_function(best_solution))

请注意，这只是一个简单的示例代码，需要根据具体问题进行适当的修改和调整。你需要自己定义问题的解表示方法、生成邻居解的方法以及适合问题的成本函数。