蚁群算法解决三维TSP问题代码

以下是基于蚁群算法的三维TSP问题的代码实现：

import numpy as np

class AntColonyTSP:
    def __init__(self, num_ants, generations, alpha, beta, rho, Q, cities):
        self.num_ants = num_ants # 蚂蚁数量
        self.generations = generations # 迭代次数
        self.alpha = alpha # 信息素重要程度因子
        self.beta = beta # 启发函数重要程度因子
        self.rho = rho # 信息素挥发因子
        self.Q = Q # 常数因子
        self.cities = cities # 城市坐标
        self.num_cities = len(cities) # 城市数量
        self.distances = self.calculate_distances() # 计算城市间距离矩阵
        self.pheromone = np.ones((self.num_cities, self.num_cities)) / self.num_cities # 初始化信息素矩阵
        self.best_path = None # 最优路径
        self.best_distance = np.inf # 最优路径长度
    
    def calculate_distances(self):
        distances = np.zeros((self.num_cities, self.num_cities))
        for i in range(self.num_cities):
            for j in range(i+1, self.num_cities):
                distances[i][j] = np.sqrt(sum((self.cities[i]-self.cities[j])**2))
                distances[j][i] = distances[i][j]
        return distances
    
    def run(self):
        for gen in range(self.generations):
            all_paths = []
            for ant in range(self.num_ants):
                path = self.generate_path()
                all_paths.append((path, self.calculate_distance(path)))
                if all_paths[-1][1] < self.best_distance:
                    self.best_path = all_paths[-1][0]
                    self.best_distance = all_paths[-1][1]
            self.update_pheromone(all_paths)
    
    def generate_path(self):
        path = []
        visited = set()
        current_city = np.random.randint(self.num_cities)
        path.append(current_city)
        visited.add(current_city)
        while len(path) < self.num_cities:
            next_city = self.select_next_city(current_city, visited)
            path.append(next_city)
            visited.add(next_city)
            current_city = next_city
        return path
    
    def select_next_city(self, current_city, visited):
        pheromone = np.copy(self.pheromone[current_city])
        pheromone[list(visited)] = 0
        probabilities = pheromone ** self.alpha * (1.0 / self.distances[current_city]) ** self.beta
        probabilities = probabilities / np.sum(probabilities)
        next_city = np.random.choice(range(self.num_cities), p=probabilities)
        return next_city
    
    def calculate_distance(self, path):
        distance = 0
        for i in range(self.num_cities-1):
            distance += self.distances[path[i]][path[i+1]]
        distance += self.distances[path[-1]][path[0]]
        return distance
    
    def update_pheromone(self, all_paths):
        self.pheromone *= self.rho
        for path, distance in all_paths:
            for i in range(self.num_cities-1):
                self.pheromone[path[i]][path[i+1]] += self.Q / distance
            self.pheromone[path[-1]][path[0]] += self.Q / distance

其中，`num_ants`表示蚂蚁数量，`generations`表示迭代次数，`alpha`、`beta`、`rho`、`Q`分别为信息素重要程度因子、启发函数重要程度因子、信息素挥发因子和常数因子，`cities`是城市坐标列表。在`run`方法中，首先生成`num_ants`条路径，并计算它们的长度，然后更新信息素矩阵。在`generate_path`方法中，通过选择下一个未访问的城市来生成一条路径。在`select_next_city`方法中，计算出每个未访问的城市被选择的概率，并返回下一个城市。在`calculate_distance`方法中，计算一条路径的长度。在`update_pheromone`方法中，根据路径长度更新信息素矩阵。最后，运行`run`方法即可得到最优路径和最优路径长度。