人工蜂群算法路径规划代码

以下是使用Python实现的人工蜂群算法路径规划代码：

import math
import random

# 定义起点和终点
start_point = (0, 0)
end_point = (10, 10)

# 定义蜜蜂数量和迭代次数
num_bees = 50
num_iter = 100

# 定义信息素浓度
pheromone = 1.0

# 定义位置更新参数
alpha = 1.0
beta = 2.0

# 定义信息素更新参数
q = 1.0
rho = 0.5

# 定义蜜蜂类
class Bee:
    def __init__(self):
        self.position = start_point
        self.distance = self.get_distance()
        
    def get_distance(self):
        # 计算蜜蜂到达终点的距离
        x_diff = self.position[0] - end_point[0]
        y_diff = self.position[1] - end_point[1]
        return math.sqrt(x_diff**2 + y_diff**2)
    
    def update_position(self, other_bees):
        # 更新蜜蜂位置
        max_distance = max(other_bees, key=lambda bee: bee.distance).distance
        if max_distance == 0:
            max_distance = 1
        prob = [(pheromone / (bee.distance / max_distance)**beta) for bee in other_bees]
        prob = [p / sum(prob) for p in prob]
        selected_bee = random.choices(other_bees, weights=prob)[0]
        self.position = (
            self.position[0] + alpha * (selected_bee.position[0] - self.position[0]),
            self.position[1] + alpha * (selected_bee.position[1] - self.position[1])
        )
        
    def update_pheromone(self):
        # 更新信息素浓度
        pheromone_change = q / self.distance
        pheromone_change = pheromone_change if self.distance != 0 else 1
        pheromone_change = pheromone_change**rho
        global pheromone
        pheromone = (1 - rho) * pheromone + rho * pheromone_change

# 初始化蜜蜂群
bees = [Bee() for _ in range(num_bees)]

# 迭代寻找最优路径
for i in range(num_iter):
    # 更新蜜蜂位置
    for bee in bees:
        other_bees = [b for b in bees if b != bee]
        bee.update_position(other_bees)
        bee.distance = bee.get_distance()
    
    # 更新信息素浓度
    for bee in bees:
        bee.update_pheromone()
    
    # 输出当前最优路径
    best_bee = min(bees, key=lambda bee: bee.distance)
    print('Iteration {}: Best distance = {}'.format(i+1, best_bee.distance))
    
# 输出最终最优路径
best_bee = min(bees, key=lambda bee: bee.distance)
print('Best path:', start_point, end_point)
for bee in bees:
    if bee != best_bee:
        continue
    print(bee.position)

以上代码实现了人工蜂群算法的基本流程，包括初始化蜜蜂群、迭代寻找最优路径以及更新蜜蜂位置和信息素浓度等操作。在迭代过程中，每次输出当前最优路径的距离，最终输出最优路径的起点、终点以及经过的所有点。需要注意的是，这里的路径是由一只蜜蜂经过的所有点构成的，而不是由所有蜜蜂共同构成的路径。