请基于蚁群算法并用python结合matplotlib写一个在二维栅格图的避障的最优路径的可视化代码

好的，以下是使用蚁群算法实现在二维栅格图上的避障最优路径可视化的Python代码：

import random
import math
import matplotlib.pyplot as plt

# 定义栅格地图尺寸和障碍物位置
MAP_WIDTH = 50
MAP_HEIGHT = 50
OBSTACLE_LIST = [(25, 25), (26, 25), (27, 25), (28, 25)]

# 定义蚂蚁数量
ANT_COUNT = 50

# 定义迭代次数
ITERATION_COUNT = 200

# 定义信息素挥发和更新速率
PHEROMONE_EVAPORATION = 0.3
PHEROMONE_UPDATE_RATE = 0.8

# 定义信息素和启发式函数的权重
PHEROMONE_WEIGHT = 1.0
HEURISTIC_WEIGHT = 2.0

# 定义启发式函数的参数
HEURISTIC_EXPONENT = 2.0


class Ant:
    def __init__(self):
        self.path = [(0, 0)]
        self.visited = set()
        self.visited.add((0, 0))

    def move(self, pheromone_map):
        current_position = self.path[-1]
        neighbors = self.get_neighbors(current_position)
        probabilities = []
        total_probability = 0.0
        for neighbor in neighbors:
            if neighbor in self.visited:
                continue
            probability = self.calculate_probability(current_position, neighbor, pheromone_map)
            probabilities.append((neighbor, probability))
            total_probability += probability
        if total_probability == 0.0:
            return
        probabilities = [(n, p / total_probability) for n, p in probabilities]
        target, _ = random.choices(probabilities)[0]
        self.path.append(target)
        self.visited.add(target)

    def get_neighbors(self, position):
        x, y = position
        neighbors = []
        if x > 0:
            neighbors.append((x - 1, y))
        if x < MAP_WIDTH - 1:
            neighbors.append((x + 1, y))
        if y > 0:
            neighbors.append((x, y - 1))
        if y < MAP_HEIGHT - 1:
            neighbors.append((x, y + 1))
        return neighbors

    def calculate_probability(self, current_position, neighbor, pheromone_map):
        pheromone = pheromone_map[neighbor[0]][neighbor[1]]
        distance = self.calculate_distance(current_position, neighbor)
        heuristic = self.calculate_heuristic(neighbor)
        probability = math.pow(pheromone, PHEROMONE_WEIGHT) * math.pow(heuristic, HEURISTIC_WEIGHT)
        probability /= math.pow(distance, HEURISTIC_EXPONENT)
        return probability

    def calculate_distance(self, position1, position2):
        x1, y1 = position1
        x2, y2 = position2
        return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)

    def calculate_heuristic(self, position):
        x, y = position
        min_distance = float('inf')
        for obstacle in OBSTACLE_LIST:
            distance = self.calculate_distance(position, obstacle)
            if distance < min_distance:
                min_distance = distance
        return 1.0 / (min_distance + 1.0)


def initialize_pheromone_map():
    return [[1.0] * MAP_HEIGHT for _ in range(MAP_WIDTH)]


def update_pheromone_map(pheromone_map, ant_list):
    for i in range(len(pheromone_map)):
        for j in range(len(pheromone_map[0])):
            pheromone_map[i][j] *= PHEROMONE_EVAPORATION
    for ant in ant_list:
        for i in range(len(ant.path) - 1):
            position1 = ant.path[i]
            position2 = ant.path[i + 1]
            pheromone_map[position1[0]][position1[1]] += PHEROMONE_UPDATE_RATE


def run_ant_colony_optimization():
    ant_list = [Ant() for _ in range(ANT_COUNT)]
    pheromone_map = initialize_pheromone_map()
    best_path = []
    best_distance = float('inf')
    for i in range(ITERATION_COUNT):
        for ant in ant_list:
            ant.move(pheromone_map)
        update_pheromone_map(pheromone_map, ant_list)
        for ant in ant_list:
            if ant.path[-1] == (MAP_WIDTH - 1, MAP_HEIGHT - 1):
                distance = len(ant.path)
                if distance < best_distance:
                    best_distance = distance
                    best_path = ant.path
    return best_path


def plot_path(path):
    obstacle_x, obstacle_y = zip(*OBSTACLE_LIST)
    path_x, path_y = zip(*path)
    plt.scatter(obstacle_x, obstacle_y, color='red')
    plt.plot(path_x, path_y, color='blue', linewidth=2.0)
    plt.xlim(0, MAP_WIDTH)
    plt.ylim(0, MAP_HEIGHT)
    plt.show()


if __name__ == '__main__':
    best_path = run_ant_colony_optimization()
    plot_path(best_path)

这段代码将生成一个二维栅格图，其中红点表示障碍物，蓝线表示在避开障碍物的情况下最优路径。您可以根据需要调整栅格地图的尺寸、障碍物位置、蚂蚁数量、迭代次数、信息素挥发和更新速率、信息素和启发式函数的权重以及启发式函数的参数。