写一个粒子群算法解决TSP问题的PYTHON代码

好的，下面是一个简单的粒子群算法解决TSP问题的Python代码：

import random
import math

# 定义TSP问题的数据
tsp_data = [
    (200, 800),
    (3600, 2300),
    (3100, 3300),
    (4700, 5750),
    (5400, 5750),
    (5608, 7103),
    (4493, 7102),
    (3600, 6950),
    (3100, 7250),
    (4700, 8450),
    (5400, 8450),
    (5610, 10053),
    (4492, 10052),
    (3600, 10800),
    (3100, 11100),
    (4700, 12300),
    (5400, 12300),
    (6650, 10803),
    (6908, 9653),
    (7510, 11200),
    (7600, 7050),
    (6650, 6050),
    (6900, 4650),
    (7510, 5000),
    (7600, 2000),
    (6650, 1500),
    (6900, 1000),
    (7510, 200),
    (3600, 200),
    (3100, 1000),
    (4700, 1750),
    (5400, 1750),
    (5608, 3103),
    (4493, 3102),
    (3600, 2500),
    (3100, 2700),
    (4700, 3850),
    (5400, 3850),
    (5610, 5053),
    (4492, 5052),
    (3600, 4500),
    (3100, 4700),
    (4700, 6050),
    (5400, 6050),
    (6650, 5053),
    (6908, 3703),
    (7510, 4500),
    (7600, 2500),
]

# 定义粒子群算法的参数
num_particles = 50
max_iterations = 1000
c1 = 1.5
c2 = 1.5
w = 0.7

# 计算两个点之间的距离
def distance(p1, p2):
    return math.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2)

# 计算一个路线的总距离
def total_distance(route):
    return sum(distance(route[i], route[i+1]) for i in range(len(route)-1)) + distance(route[0], route[-1])

# 初始化粒子群
particles = []
best_particle = None
best_particle_fitness = float('inf')
for i in range(num_particles):
    particle = random.sample(tsp_data, len(tsp_data))
    fitness = total_distance(particle)
    particles.append({'route': particle, 'fitness': fitness, 'velocity': [0] * len(tsp_data)})
    if fitness < best_particle_fitness:
        best_particle = particle
        best_particle_fitness = fitness

# 开始迭代
for i in range(max_iterations):
    for j in range(num_particles):
        particle = particles[j]
        velocity = particle['velocity']
        route = particle['route']
        fitness = particle['fitness']
        personal_best = route.copy()

        # 更新粒子的速度和位置
        for k in range(len(route)):
            velocity[k] = w * velocity[k] + c1 * random.random() * (personal_best[k][0] - route[k][0]) \
                          + c2 * random.random() * (best_particle[k][0] - route[k][0])
            route[k] = (route[k][0] + velocity[k], route[k][1])

        # 计算新的路线的总距离
        new_fitness = total_distance(route)

        # 更新粒子的个人最优解和全局最优解
        if new_fitness < fitness:
            particle['fitness'] = new_fitness
            particle['route'] = route
            personal_best = route.copy()
            if new_fitness < best_particle_fitness:
                best_particle_fitness = new_fitness
                best_particle = route.copy()

print("最优解路线：", best_particle)
print("最优解总距离：", best_particle_fitness)

在这个代码中，我们定义了一个TSP问题的数据，即每个城市的坐标。然后我们定义了粒子群算法的参数，包括粒子数量、最大迭代次数以及加速度系数等。

在初始化粒子群的时候，我们随机生成了每个粒子的初始路线，并计算了其总距离。然后在每次迭代中，我们更新每个粒子的速度和位置，并计算新的路线的总距离。如果新的路线比个人最优解或全局最优解更优，则更新个人最优解和全局最优解。

最后，我们输出了最优解的路线和总距离。