利用蚁群算法求解三维点坐标的TSP模型的最佳任务序列并以图像形式输出的代码

以下是基于Python语言的蚁群算法求解三维点坐标的TSP模型的最佳任务序列并以图像形式输出的代码：

import numpy as np
import matplotlib.pyplot as plt

# 定义城市类
class City:
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

# 定义蚂蚁类
class Ant:
    def __init__(self, alpha, beta, pheromone, cities):
        self.alpha = alpha # alpha参数
        self.beta = beta # beta参数
        self.pheromone = pheromone # 信息素矩阵
        self.cities = cities # 城市列表
        self.tour = [] # 路线
        self.distance = 0.0 # 路线长度

    # 选择下一个城市
    def select_next_city(self):
        probs = self.calculate_probabilities()
        rand = np.random.uniform()
        total = 0.0
        for i in range(len(self.cities)):
            if rand <= total + probs[i]:
                return i
            total += probs[i]
        return np.random.randint(0, len(self.cities))

    # 计算概率
    def calculate_probabilities(self):
        probs = []
        for i in range(len(self.cities)):
            if i in self.tour:
                probs.append(0.0)
            else:
                pheromone = self.pheromone[self.tour[-1]][i]
                distance = self.calculate_distance(self.tour[-1], i)
                prob = np.power(pheromone, self.alpha) * np.power(1.0 / distance, self.beta)
                probs.append(prob)
        total_prob = sum(probs)
        return [p / total_prob for p in probs]

    # 计算距离
    def calculate_distance(self, city1, city2):
        x1, y1, z1 = self.cities[city1].x, self.cities[city1].y, self.cities[city1].z
        x2, y2, z2 = self.cities[city2].x, self.cities[city2].y, self.cities[city2].z
        return np.sqrt(np.power(x1 - x2, 2) + np.power(y1 - y2, 2) + np.power(z1 - z2, 2))

    # 计算路线长度
    def calculate_tour_length(self):
        length = 0.0
        for i in range(len(self.tour)):
            length += self.calculate_distance(self.tour[i], self.tour[(i+1)%len(self.tour)])
        return length

    # 蚂蚁搜索
    def search(self):
        # 初始化路线和路线长度
        self.tour = [np.random.randint(0, len(self.cities))]
        self.distance = 0.0
        # 搜索过程
        while len(self.tour) < len(self.cities):
            next_city = self.select_next_city()
            self.tour.append(next_city)
            self.distance += self.calculate_distance(self.tour[-2], next_city)
        self.distance += self.calculate_distance(self.tour[-1], self.tour[0])

# 初始化城市和蚂蚁
cities = [City(np.random.uniform(), np.random.uniform(), np.random.uniform()) for i in range(50)]
ants = [Ant(alpha=1.0, beta=2.0, pheromone=np.ones((len(cities), len(cities))), cities=cities) for i in range(10)]

# 迭代搜索
best_distance = np.inf
best_tour = []
for i in range(100):
    for ant in ants:
        ant.search()
        if ant.distance < best_distance:
            best_distance = ant.distance
            best_tour = ant.tour
    # 更新信息素矩阵
    pheromone = np.zeros((len(cities), len(cities)))
    for ant in ants:
        for i in range(len(ant.tour)):
            j = (i+1) % len(ant.tour)
            pheromone[ant.tour[i]][ant.tour[j]] += 1.0 / ant.distance
            pheromone[ant.tour[j]][ant.tour[i]] = pheromone[ant.tour[i]][ant.tour[j]]
    for i in range(len(cities)):
        for j in range(len(cities)):
            pheromone[i][j] = (1 - 0.1) * pheromone[i][j] + 0.1 * np.ones((1, 1))) # 蒸发信息素
    for ant in ants:
        ant.pheromone = pheromone

# 输出结果
x, y, z = [], [], []
for city in cities:
    x.append(city.x)
    y.append(city.y)
    z.append(city.z)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z)
for i in range(len(best_tour)):
    j = (i+1) % len(best_tour)
    ax.plot([cities[best_tour[i]].x, cities[best_tour[j]].x], 
            [cities[best_tour[i]].y, cities[best_tour[j]].y], 
            [cities[best_tour[i]].z, cities[best_tour[j]].z], 'r-')
plt.show()

该代码中使用了numpy和matplotlib库，需要提前安装。运行结果将显示出50个随机生成的三维点坐标以及最佳任务序列。