帮我生成用蚁群算法解决旅行商问题的python代码

好的，我来为您生成一个用蚁群算法解决旅行商问题的python代码。

import numpy as np
import random

class AntColony:
    def __init__(self, cities, num_ants, iterations):
        self.cities = cities
        self.num_ants = num_ants
        self.iterations = iterations
        self.pheromone_matrix = np.ones((len(cities), len(cities))) / len(cities)
        self.distance_matrix = self.get_distance_matrix(cities)
        self.best_path = None
        self.best_path_distance = float('inf')
    
    def get_distance_matrix(self, cities):
        matrix = np.zeros((len(cities), len(cities)))
        for i in range(len(cities)):
            for j in range(len(cities)):
                if i != j:
                    matrix[i][j] = np.linalg.norm(cities[i] - cities[j])
        return matrix
    
    def update_pheromone_matrix(self, ants):
        for i, ant in enumerate(ants):
            for j in range(len(ant.path) - 1):
                self.pheromone_matrix[ant.path[j]][ant.path[j+1]] += 1 / ant.distance
                self.pheromone_matrix[ant.path[j+1]][ant.path[j]] += 1 / ant.distance
        self.pheromone_matrix = (1 - self.rho) * self.pheromone_matrix
    
    def next_city(self, ant):
        prob = np.zeros(len(self.cities))
        total = 0
        for i, visited in enumerate(ant.visited):
            if not visited:
                prob[i] = (self.pheromone_matrix[ant.current][i] ** self.alpha) * ((1 / self.distance_matrix[ant.current][i]) ** self.beta)
                total += prob[i]
        for i in range(len(prob)):
            prob[i] /= total
        return np.random.choice(len(self.cities), p=prob)
    
    def run(self, alpha=1, beta=5, rho=0.5):
        self.alpha = alpha
        self.beta = beta
        self.rho = rho
        for iteration in range(self.iterations):
            ants = [Ant(self) for i in range(self.num_ants)]
            for ant in ants:
                while len(ant.path) != len(self.cities):
                    next_city = self.next_city(ant)
                    ant.visited[next_city] = True
                    ant.path.append(next_city)
                    ant.distance += self.distance_matrix[ant.current][next_city]
                    ant.current = next_city
                ant.distance