蚁群算法航迹规划问题的代码

蚁群算法是一种基于模拟蚂蚁觅食行为的一种优化算法，适用于许多问题的求解，包括航迹规划问题。下面是一个简单的航迹规划问题的蚁群算法代码：

import numpy as np

class Ant:
    def __init__(self, start, goal, alpha, beta, pheromone, distance):
        self.start = start
        self.goal = goal
        self.alpha = alpha
        self.beta = beta
        self.pheromone = pheromone
        self.distance = distance
        self.path = [start]

    def add_node(self, node):
        self.path.append(node)

    def update_pheromone(self):
        path_distance = self.path_distance()
        for i in range(len(self.path)-1):
            node1 = self.path[i]
            node2 = self.path[i+1]
            self.pheromone[node1][node2] += 1 / path_distance
            self.pheromone[node2][node1] += 1 / path_distance

    def path_distance(self):
        distance = 0
        for i in range(len(self.path)-1):
            distance += self.distance[self.path[i]][self.path[i+1]]
        return distance

class ACO:
    def __init__(self, start, goal, nodes, distance, ant_count, alpha, beta, rho, q):
        self.start = start
        self.goal = goal
        self.nodes = nodes
        self.distance = distance
        self.ant_count = ant_count
        self.alpha = alpha
        self.beta = beta
        self.rho = rho
        self.q = q
        self.pheromone = np.ones(distance.shape) / len(nodes)
        self.ants = [Ant(start, goal, alpha, beta, self.pheromone, distance) for i in range(ant_count)]
        self.best_path = None
        self.best_distance = float("inf")

    def run(self, iterations):
        for i in range(iterations):
            for ant in self.ants:
                while ant.path[-1] != self.goal:
                    ant.add_node(self.next_node(ant))
                distance = ant.path_distance()
                if distance < self.best_distance:
                    self.best_distance = distance
                    self.best_path = ant.path
                ant.update_pheromone()
                ant.path = [self.start]

            self.pheromone *= self.rho
            for i in range(len(self.nodes)):
                for j in range(len(self.nodes)):
                    for ant in self.ants:
                        if j in ant.path and i in ant.path:
                            self.pheromone[i][j] += self.q / ant.path_distance()

    def next_node(self, ant):
        current_node = ant.path[-1]
        unvisited_nodes = [i for i in range(len(self.nodes)) if i not in ant.path]
        probabilities = [((self.pheromone[current_node][i] ** self.alpha) * ((1/self.distance[current_node][i]) ** self.beta)) for i in unvisited_nodes]
        probabilities = probabilities / np.sum(probabilities)
        next_node = np.random.choice(unvisited_nodes, p=probabilities)
        return next_node

这里的输入参数包括起点、终点、航点列表、航点之间的距离矩阵、蚂蚁数量、alpha、beta、信息素挥发系数rho和信息素增量q。ACO类的run方法是算法的主要部分，包括每只蚂蚁的路径搜索、信息素更新和信息素挥发等操作。其中，next_node方法用于选择下一个航点，根据信息素和距离计算航点的选择概率。最终，ACO算法将得到一条最优路径，即从起点到终点的最短航迹。