在三维空间中利用蚁群算法求解TSP模型并将最优任务序列以图像形式输出的python代码

以下是基于Python的蚁群算法求解TSP问题的代码示例：

import numpy as np
import matplotlib.pyplot as plt

# TSP问题参数
n_cities = 10  # 城市数量
n_ants = 20  # 蚂蚁数量
alpha = 1  # 启发因子
beta = 5  # 期望因子
rho = 0.1  # 信息素挥发因子
Q = 100  # 信息素增强强度
iteration = 100  # 迭代次数

# 城市坐标生成
cities = np.random.rand(n_cities, 2) * 100

# 距离矩阵计算
distances = np.zeros((n_cities, n_cities))
for i in range(n_cities):
    for j in range(i + 1, n_cities):
        distances[i][j] = np.sqrt(np.sum((cities[i] - cities[j]) ** 2))
        distances[j][i] = distances[i][j]

# 初始化信息素矩阵
pheromones = np.zeros((n_cities, n_cities))

# 蚂蚁类
class Ant:
    def __init__(self):
        self.path = [0]
        self.visited = [False] * n_cities
        self.visited[0] = True
        self.distance = 0

    # 转移概率计算
    def calc_prob(self):
        cur_city = self.path[-1]
        probs = np.zeros(n_cities)
        for next_city in range(n_cities):
            if self.visited[next_city]:
                continue
            probs[next_city] = pheromones[cur_city][next_city] ** alpha * (1.0 / distances[cur_city][next_city]) ** beta
        probs = probs / np.sum(probs)
        return probs

    # 转移
    def transition(self):
        probs = self.calc_prob()
        next_city = np.random.choice(range(n_cities), p=probs)
        self.path.append(next_city)
        self.visited[next_city] = True
        self.distance += distances[self.path[-2]][self.path[-1]]

    # 信息素增强
    def enhance_pheromones(self):
        for i in range(n_cities - 1):
            pheromones[self.path[i]][self.path[i + 1]] += Q / self.distance

    # 重置
    def reset(self):
        self.path = [0]
        self.visited = [False] * n_cities
        self.visited[0] = True
        self.distance = 0

# 迭代
best_distance = float('inf')
best_path = None
for i in range(iteration):
    ants = [Ant() for _ in range(n_ants)]
    for ant in ants:
        for _ in range(n_cities - 1):
            ant.transition()
        ant.distance += distances[ant.path[-1]][ant.path[0]]
        if ant.distance < best_distance:
            best_distance = ant.distance
            best_path = ant.path
        ant.enhance_pheromones()
    pheromones *= (1 - rho)
    pheromones += rho * np.ones((n_cities, n_cities)) * (Q / best_distance)

# 输出最优路径图像
fig, ax = plt.subplots()
ax.scatter(cities[:, 0], cities[:, 1])
for i in range(n_cities - 1):
    ax.plot([cities[best_path[i]][0], cities[best_path[i + 1]][0]], [cities[best_path[i]][1], cities[best_path[i + 1]][1]])
ax.plot([cities[best_path[-1]][0], cities[best_path[0]][0]], [cities[best_path[-1]][1], cities[best_path[0]][1]])
plt.show()

该代码将生成一个随机的城市坐标，利用蚁群算法求解TSP问题，并将最优路径以图像形式输出。您可以根据需要修改参数和城市坐标。