蚁群算法用python代码实现蚂蚁移动的动画过程，给出代码

以下是蚁群算法用Python实现蚂蚁移动的动画过程的示例代码：

import random
import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

# 初始化常数
NUM_ANTS = 10
NUM_ITERATIONS = 100
ALPHA = 1
BETA = 2
RHO = 0.5
Q = 100
INITIAL_PHEROMONE = 0.1
CITIES = np.array([
    [0.4000, 0.4439],
    [0.2439, 0.1463],
    [0.1707, 0.2293],
    [0.2293, 0.7610],
    [0.5171, 0.9414],
    [0.8732, 0.6536],
    [0.6878, 0.5219],
    [0.8488, 0.3609],
    [0.6683, 0.2536],
    [0.6195, 0.2634]
])

# 初始化信息素矩阵
pheromone = np.ones((10, 10)) * INITIAL_PHEROMONE

# 计算城市之间的距离
def calculate_distance(city1, city2):
    return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)

# 更新信息素矩阵
def update_pheromone(trails, distances):
    pheromone *= (1 - RHO)
    for i in range(NUM_ANTS):
        for j in range(9):
            start, end = trails[i][j], trails[i][j + 1]
            pheromone[start][end] += Q / distances[i]

# 计算蚂蚁的路径和距离
def calculate_ant_path(start_city, distances):
    ant_path = np.zeros((NUM_ANTS, 10), dtype=int)
    ant_path[:, 0] = start_city
    ant_distance = np.zeros(NUM_ANTS)

    for i in range(NUM_ITERATIONS):
        for j in range(NUM_ANTS):
            current_city = ant_path[j][i]
            unvisited_cities = [x for x in range(10) if x not in ant_path[j][:i]]
            probabilities = [math.pow(pheromone[current_city][x], ALPHA) * math.pow(1 / distances[current_city][x], BETA) for x in unvisited_cities]
            probabilities /= np.sum(probabilities)
            next_city = np.random.choice(unvisited_cities, p=probabilities)
            ant_path[j][i + 1] = next_city
            ant_distance[j] += distances[current_city][next_city]

        update_pheromone(ant_path, ant_distance)

    return ant_path, ant_distance

# 绘制动画
def animate(i):
    plt.clf()
    plt.title("Ant Colony Optimization")

    ant_path, ant_distance = calculate_ant_path(0, distances)
    best_ant_idx = np.argmin(ant_distance)
    best_ant_path = ant_path[best_ant_idx]
    best_distance = ant_distance[best_ant_idx]

    plt.plot(CITIES[:, 0], CITIES[:, 1], 'o', color='black')
    plt.plot(CITIES[best_ant_path, 0], CITIES[best_ant_path, 1], 'o-', color='red')
    plt.text(0.05, 0.05, "Iteration: " + str(i) + "\nBest Distance: " + str(best_distance), fontsize=12)

# 主程序
if __name__ == '__main__':
    distances = np.zeros((10, 10))
    for i in range(10):
        for j in range(10):
            distances[i][j] = calculate_distance(CITIES[i], CITIES[j])

    fig = plt.figure(figsize=(6, 6))
    animation = FuncAnimation(fig, animate, frames=50, interval=500)
    plt.show()

该代码使用matplotlib库绘制动画，并模拟了蚂蚁在城市间移动的过程。在动画的每一帧中，都会根据当前信息素矩阵和距离矩阵计算出蚂蚁的路径和距离，并绘制出最优路径和当前迭代的信息。