self.distance_m = np.zeros((self.num, self.num))

这行代码是在初始化一个 num x num 大小的全零矩阵，用来表示 num 个元素之间的距离。其中 np.zeros() 函数用来创建一个全零数组，参数是一个元组，表示数组的形状。在这里，形状为 (num, num) 表示创建一个 num 行 num 列的二维数组。然后这个全零数组被赋值给 self.distance_m 变量，表示这个矩阵是类的一个属性。

相关问题

import pandas as pd import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap from scipy.spatial.distance import cdist from ant_colony import solve_tsp # 读取城市数据 df = pd.read_excel('world_coordinate.xlsx', index_col=0, dtype=str) # 提取城市和经纬度数据 countrys = df.index.values countrys_coords = np.array(df['[longitude, latitude]'].apply(eval).tolist()) # 计算城市间的距离矩阵 dist_matrix = cdist(countrys_coords, countrys_coords, metric='euclidean') # 创建蚁群算法实例 num_ants = 50 num_iterations = 500 alpha = 1 beta = 2 rho = 0.5 acs = solve_tsp(dist_matrix, num_ants=num_ants, num_iterations=num_iterations, alpha=alpha, beta=beta, rho=rho) # 输出访问完所有城市的最短路径的距离和城市序列 best_path = acs.get_best_path() best_distance = acs.best_cost visited_cities = [countrys[i] for i in best_path] print("最短路径距离：", best_distance) print("访问城市序列：", visited_cities) # 数据可视化 fig = plt.figure(figsize=(12, 8)) map = Basemap(projection='robin', lat_0=0, lon_0=0, resolution='l') map.drawcoastlines(color='gray') map.drawcountries(color='gray') x, y = map(countrys_coords[:, 0], countrys_coords[:, 1]) map.scatter(x, y, c='b', marker='o') path_coords = countrys_coords[best_path] path_x, path_y = map(path_coords[:, 0], path_coords[:, 1]) map.plot(path_x, path_y, c='r', marker='o') for i in range(len(countrys)): x, y = map(countrys_coords[i, 1], countrys_coords[i, 0]) plt.text(x, y, countrys[i], fontproperties='SimHei', color='black', fontsize=8, ha='center', va='center') plt.title("全球首都最短路径规划") plt.show()改成现在都有调用蚁群算法库的代码

好的，可以尝试使用蚁群算法库来计算最优路径。以下是修改后的代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from ant_colony import AntColonyOptimizer

# 读取城市数据
df = pd.read_excel('world_coordinate.xlsx', index_col=0, dtype=str)

# 提取城市和经纬度数据
countrys = df.index.values
countrys_coords = np.array(df['[longitude, latitude]'].apply(eval).tolist())

# 创建蚁群算法实例
num_ants = 50
num_iterations = 500
alpha = 1
beta = 2
rho = 0.5
optimizer = AntColonyOptimizer(num_ants=num_ants, num_iterations=num_iterations, alpha=alpha, beta=beta, rho=rho)

# 计算最短路径
best_path, best_distance = optimizer.solve(countrys_coords)

# 输出访问完所有城市的最短路径的距离和城市序列
visited_cities = [countrys[i] for i in best_path]
print("最短路径距离：", best_distance)
print("访问城市序列：", visited_cities)

# 数据可视化
fig = plt.figure(figsize=(12, 8))
map = Basemap(projection='robin', lat_0=0, lon_0=0, resolution='l')
map.drawcoastlines(color='gray')
map.drawcountries(color='gray')
x, y = map(countrys_coords[:, 0], countrys_coords[:, 1])
map.scatter(x, y, c='b', marker='o')
path_coords = countrys_coords[best_path]
path_x, path_y = map(path_coords[:, 0], path_coords[:, 1])
map.plot(path_x, path_y, c='r', marker='o')
for i in range(len(countrys)):
    x, y = map(countrys_coords[i, 1], countrys_coords[i, 0])
    plt.text(x, y, countrys[i], fontproperties='SimHei', color='black', fontsize=8, ha='center', va='center')
plt.title("全球首都最短路径规划")
plt.show()

其中，`AntColonyOptimizer` 是一个自定义的蚁群算法优化器类，代码如下：

import numpy as np

class AntColonyOptimizer:
    def __init__(self, num_ants, num_iterations, alpha, beta, rho, Q=100):
        self.num_ants = num_ants
        self.num_iterations = num_iterations
        self.alpha = alpha
        self.beta = beta
        self.rho = rho
        self.Q = Q

    def solve(self, dist_matrix):
        n = dist_matrix.shape[0]

        # 初始化信息素矩阵
        tau = np.ones((n, n))

        # 创建蚂蚁
        ants = np.zeros((self.num_ants, n), dtype=int)

        # 记录最优路径和距离
        best_path = None
        best_distance = np.inf

        # 迭代搜索
        for iter in range(self.num_iterations):
            # 初始化蚂蚁位置
            ants[:, 0] = np.random.randint(0, n, size=self.num_ants)

            # 蚂蚁移动
            for k in range(1, n):
                # 计算可选城市的概率
                probs = np.zeros((self.num_ants, n))
                for i in range(self.num_ants):
                    curr_city = ants[i, k-1]
                    visited = ants[i, :k]
                    unvisited = np.setdiff1d(range(n), visited)
                    if len(unvisited) == 0:
                        continue
                    pheromone = tau[curr_city, unvisited]
                    distance = dist_matrix[curr_city, unvisited]
                    probs[i, unvisited] = pheromone ** self.alpha * (1 / distance) ** self.beta
                    probs[i, visited] = 0
                    probs[i] /= probs[i].sum()

                # 选择下一个城市
                ants[:, k] = [np.random.choice(range(n), p=probs[i]) for i in range(self.num_ants)]

            # 计算每只蚂蚁的路径长度
            path_lengths = np.zeros(self.num_ants)
            for i in range(self.num_ants):
                path = ants[i]
                path_lengths[i] = dist_matrix[path[-1], path[0]] + dist_matrix[path[:-1], path[1:]].sum()

                # 更新最优路径
                if path_lengths[i] < best_distance:
                    best_distance = path_lengths[i]
                    best_path = path

            # 更新信息素矩阵
            delta_tau = np.zeros((n, n))
            for i in range(self.num_ants):
                path = ants[i]
                for j in range(n-1):
                    curr_city = path[j]
                    next_city = path[j+1]
                    delta_tau[curr_city, next_city] += self.Q / path_lengths[i]
                delta_tau[path[-1], path[0]] += self.Q / path_lengths[i]
            tau = (1 - self.rho) * tau + self.rho * delta_tau

        return best_path, best_distance

这个优化器使用了与之前相同的距离矩阵，不同的是，它将蚂蚁移动和信息素更新的过程封装在了 `solve` 方法中，返回最优路径和距离。

旅行商问题 蚁群算法 python

以下是使用蚁群算法解决旅行商问题的Python代码示例：

import numpy as np

# 城市坐标
cities = np.array([[1304, 2312], [3639, 1315], [4177, 2244], [3712, 1399], [3488, 1535], [3326, 1556], [3238, 1229], [4196, 1004], [4312, 790], [4386, 570], [3007, 1970], [2562, 1756], [2788, 1491], [2381, 1676], [1332, 695], [3715, 1678], [3918, 2179], [4061, 2370], [3780, 2212], [3676, 2578], [4029, 2838], [4263, 2931], [3429, 1908], [3507, 2367], [3394, 2643], [3439, 3201], [2935, 3240], [3140, 3550], [2545, 2357], [2778, 2826], [2370, 2975]])

# 计算城市之间的距离
def distance(city1, city2):
    return np.sqrt(np.sum((city1 - city2) ** 2))

num_cities = len(cities)
distance_matrix = np.zeros((num_cities, num_cities))
for i in range(num_cities):
    for j in range(i+1, num_cities):
        distance_matrix[i][j] = distance(cities[i], cities[j])
        distance_matrix[j][i] = distance_matrix[i][j]

# 蚂蚁类
class Ant:
    def __init__(self, alpha, beta, distance_matrix):
        self.alpha = alpha
        self.beta = beta
        self.distance_matrix = distance_matrix
        self.num_cities = len(distance_matrix)
        self.pheromone_matrix = np.ones((self.num_cities, self.num_cities)) / (self.num_cities ** 2)
        self.path = []
        self.distance = 0.0

    # 选择下一个城市
    def select_next_city(self):
        available_cities = list(set(range(self.num_cities)) - set(self.path))
        probabilities = [self._calculate_prob(city) for city in available_cities]
        selected_city = np.random.choice(available_cities, p=probabilities)
        self.path.append(selected_city)
        self.distance += self.distance_matrix[self.path[-2]][selected_city]

    # 计算选择每个城市的概率
    def _calculate_prob(self, city):
        pheromone = self.pheromone_matrix[self.path[-1]][city]
        dist = self.distance_matrix[self.path[-1]][city]
        probs = pheromone ** self.alpha * ((1.0 / dist) ** self.beta)
        total_probs = sum([self.pheromone_matrix[self.path[-1]][i] ** self.alpha * ((1.0 / self.distance_matrix[self.path[-1]][i]) ** self.beta) for i in range(self.num_cities) if i not in self.path])
        return probs / total_probs

    # 更新信息素
    def update_pheromone(self, delta_pheromone_matrix):
        self.pheromone_matrix = (1 - evaporation_rate) * self.pheromone_matrix + delta_pheromone_matrix

# 蚁群算法类
class ACO:
    def __init__(self, num_ants, num_iterations, alpha, beta, evaporation_rate, distance_matrix):
        self.num_ants = num_ants
        self.num_iterations = num_iterations
        self.alpha = alpha
        self.beta = beta
        self.evaporation_rate = evaporation_rate
        self.distance_matrix = distance_matrix

    # 运行蚁群算法
    def run(self):
        best_path = []
        best_distance = float('inf')
        for i in range(self.num_iterations):
            ants = [Ant(self.alpha, self.beta, self.distance_matrix) for j in range(self.num_ants)]
            for ant in ants:
                for j in range(self.distance_matrix.shape[0] - 1):
                    ant.select_next_city()
                ant.distance += self.distance_matrix[ant.path[-1]][ant.path[0]]
                if ant.distance < best_distance:
                    best_distance = ant.distance
                    best_path = ant.path
                delta_pheromone_matrix = np.zeros((self.distance_matrix.shape[0], self.distance_matrix.shape[1]))
                for j in range(self.distance_matrix.shape[0] - 1):
                    delta_pheromone_matrix[ant.path[j]][ant.path[j+1]] += 1.0 / ant.distance
                    delta_pheromone_matrix[ant.path[j+1]][ant.path[j]] += 1.0 / ant.distance
                delta_pheromone_matrix[ant.path[-1]][ant.path[0]] += 1.0 / ant.distance
                delta_pheromone_matrix[ant.path[0]][ant.path[-1]] += 1.0 / ant.distance
                ant.update_pheromone(delta_pheromone_matrix)
        return best_path, best_distance

# 运行蚁群算法
num_ants = 50
num_iterations = 500
alpha = 1
beta = 5
evaporation_rate = 0.5
aco = ACO(num_ants, num_iterations, alpha, beta, evaporation_rate, distance_matrix)
best_path, best_distance = aco.run()

# 输出结果
print('最短路径为：', best_path)
print('最短距离为：', best_distance)