生成一个蚁群算法的车辆路径规划问题代码

以下是一个使用蚁群算法解决车辆路径规划问题的示例代码：

import random

class Ant:
    def __init__(self, graph, start_node):
        self.graph = graph
        self.visited_nodes = [start_node]
        self.current_node = start_node
    
    def choose_next_node(self, pheromone_map, alpha, beta):
        unvisited_nodes = [node for node in self.graph.nodes() if node not in self.visited_nodes]
        if not unvisited_nodes:
            return None
        
        total_prob = 0
        probs = []
        for node in unvisited_nodes:
            edge = (self.current_node, node)
            prob = pheromone_map[edge]**alpha * (1/self.graph[edge[0]][edge[1]]['distance'])**beta
            probs.append(prob)
            total_prob += prob
        
        cum_probs = [probs[0]/total_prob]
        for i in range(1, len(probs)):
            cum_probs.append(cum_probs[-1] + probs[i]/total_prob)
        
        next_node_idx = len(cum_probs) - 1
        r = random.random()
        for i in range(len(cum_probs)):
            if r < cum_probs[i]:
                next_node_idx = i
                break
        
        return unvisited_nodes[next_node_idx]
    
    def move_to_node(self, next_node):
        self.visited_nodes.append(next_node)
        self.current_node = next_node
    
    def path_length(self):
        length = 0
        for i in range(len(self.visited_nodes) - 1):
            length += self.graph[self.visited_nodes[i]][self.visited_nodes[i+1]]['distance']
        return length
    
def update_pheromone_map(pheromone_map, ants, Q):
    for edge in pheromone_map.keys():
        pheromone_map[edge] *= 0.5
        for ant in ants:
            if edge in zip(ant.visited_nodes, ant.visited_nodes[1:]):
                pheromone_map[edge] += Q/ant.path_length()

def ant_colony_optimization(graph, ant_count=10, alpha=1.0, beta=1.0, evaporation_rate=0.5, Q=100):
    pheromone_map = {(i,j): 1 for i,j in graph.edges()}
    ants = [Ant(graph, random.choice(list(graph.nodes()))) for _ in range(ant_count)]
    best_path = None
    best_path_length = float('inf')
    for i in range(100):
        for ant in ants:
            while True:
                next_node = ant.choose_next_node(pheromone_map, alpha, beta)
                if next_node is None:
                    break
                ant.move_to_node(next_node)
            path_length = ant.path_length()
            if path_length < best_path_length:
                best_path = ant.visited_nodes[:]
                best_path_length = path_length
        update_pheromone_map(pheromone_map, ants, Q)
        for edge in pheromone_map.keys():
            pheromone_map[edge] *= (1 - evaporation_rate)
    return best_path, best_path_length

该代码中使用的是Python编程语言，需要依赖`networkx`库，可以通过pip安装：

pip install networkx

使用示例：

import networkx as nx

G = nx.complete_graph(5)
for i,j in G.edges():
    G[i][j]['distance'] = random.randint(1,10)
    
best_path, best_path_length = ant_colony_optimization(G)
print(best_path, best_path_length)

该示例代码中使用了一个完全图，边的距离是随机生成的。你可以将其替换为你的实际车辆路径规划问题中的图和距离，然后调用`ant_colony_optimization`函数即可得到最优路径和路径长度。