模拟退火算法 选址python

模拟退火算法是一种全局优化算法，它可以在解空间中寻找全局最优解。在选址问题中，模拟退火算法可以用来寻找最优的服务站位置。下面是一个使用Python实现模拟退火算法解决选址问题的例子：

import random
import math
import csv
import time

# 读取需求点和服务站数据
def read_data(file_name):
    with open(file_name, 'r') as f:
        reader = csv.reader(f)
        data = list(reader)
    return data

# 计算距离
def distance(x1, y1, x2, y2):
    return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)

# 计算总费用
def total_cost(customers, facilities, solution):
    cost = 0
    for i, c in enumerate(customers):
        d = float('inf')
        for j, f in enumerate(facilities):
            if solution[j] == 1:
                dist = distance(c[1], c[2], f[1], f[2])
                if dist < d:
                    d = dist
        cost += d * c[3]
    for i, f in enumerate(facilities):
        if solution[i] == 1:
            cost += f[3]
    return cost

# 模拟退火算法
def simulated_annealing(customers, facilities, initial_solution, initial_temperature, cooling_rate, stopping_temperature):
    current_solution = initial_solution
    current_cost = total_cost(customers, facilities, current_solution)
    best_solution = current_solution
    best_cost = current_cost
    temperature = initial_temperature
    while temperature > stopping_temperature:
        i = random.randint(0, len(facilities) - 1)
        new_solution = current_solution.copy()
        new_solution[i] = 1 - new_solution[i]
        new_cost = total_cost(customers, facilities, new_solution)
        delta = new_cost - current_cost
        if delta < 0 or math.exp(-delta / temperature) > random.random():
            current_solution = new_solution
            current_cost = new_cost
            if current_cost < best_cost:
                best_solution = current_solution
                best_cost = current_cost
        temperature *= cooling_rate
    return best_solution, best_cost

# 保存结果到csv文件
def save_result(file_name, facilities, solution, cost, time):
    with open(file_name, 'w', newline='') as f:
        writer = csv.writer(f)
        writer.writerow(['Total cost:', cost, 'Time:', time])
        writer.writerow(['Facility status:'] + solution)
        writer.writerow(['Facility assignments:'])
        for i, f in enumerate(facilities):
            if solution[i] == 1:
                writer.writerow([i] + f[4:])

# 主函数
def main():
    # 读取数据
    customers = read_data('customers.csv')[1:]
    facilities = read_data('facilities.csv')[1:]

    # 初始化解
    initial_solution = [random.randint(0, 1) for _ in range(len(facilities))]

    # 运行模拟退火算法
    start_time = time.time()
    solution, cost = simulated_annealing(customers, facilities, initial_solution, 1000, 0.99, 1e-6)
    end_time = time.time()

    # 保存结果
    save_result('result.csv', facilities, solution, cost, end_time - start_time)

if __name__ == '__main__':
    main()

在这个例子中，我们首先读取了需求点和服务站的数据，然后定义了计算距离和总费用的函数。接着，我们实现了模拟退火算法，并在主函数中调用该算法来解决选址问题。最后，我们将结果保存到csv文件中。