写一个能运行的使用人工鱼群算法解决TSP问题的Python代码
时间: 2024-05-04 14:20:46 浏览: 10
以下是一个基于人工鱼群算法(AFSA)的TSP问题的Python代码实现:
```python
import numpy as np
import matplotlib.pyplot as plt
def distance(coord1, coord2):
return np.sqrt((coord1[0] - coord2[0])**2 + (coord1[1] - coord2[1])**2)
def generate_points(num_points):
return np.random.rand(num_points, 2)
def generate_population(num_fish, num_points):
return np.random.permutation(np.arange(num_points))[:num_fish]
def calculate_fitness(routes, points):
fitness = []
for route in routes:
total_distance = 0
for i in range(len(route)):
total_distance += distance(points[route[i]], points[route[(i+1)%len(route)]])
fitness.append(1/total_distance)
return np.array(fitness)
def move_towards(food, current_position, step_size):
direction = food - current_position
distance = np.linalg.norm(direction)
if distance == 0:
return current_position
else:
direction /= distance
return current_position + step_size * direction
def update_position(positions, population, fitness, step_size):
new_positions = []
for i in range(len(positions)):
fish = population[i]
current_position = positions[fish]
food = positions[np.argmax(fitness)]
new_position = move_towards(food, current_position, step_size)
new_positions.append(new_position)
return np.array(new_positions)
def update_population(population, fitness, num_points, step_size):
new_population = []
for i in range(len(population)):
fish = population[i]
current_position = positions[fish]
food = positions[np.argmax(fitness)]
new_position = move_towards(food, current_position, step_size)
new_index = np.argmin([distance(new_position, points[j]) for j in range(num_points) if j not in population])
new_population.append(new_index)
return np.array(new_population)
def plot_tsp(points, route):
x = points[:,0]
y = points[:,1]
plt.plot(x[route], y[route], 'r')
plt.plot(x, y, 'bo')
plt.show()
def run_afsa(num_points, num_fish, max_iterations, step_size):
# Generate random points
points = generate_points(num_points)
# Generate initial population
population = generate_population(num_fish, num_points)
# Calculate initial fitness
fitness = calculate_fitness([population], points)[0]
# Initialize positions
positions = points[population]
# Run iterations
best_fitness = []
for i in range(max_iterations):
# Update positions
positions = update_position(positions, population, fitness, step_size)
# Update population
population = update_population(population, fitness, num_points, step_size)
# Calculate fitness
fitness_new = calculate_fitness([population], points)[0]
# Update best fitness
if fitness_new > fitness:
fitness = fitness_new
best_fitness.append(fitness)
# Find best route
best_route = population[np.argmax(calculate_fitness([population], points))]
# Plot best route
plot_tsp(points, best_route)
return best_fitness
# Example usage
num_points = 20
num_fish = 10
max_iterations = 100
step_size = 0.1
run_afsa(num_points, num_fish, max_iterations, step_size)
```
其中,`generate_points`函数用于生成随机的点坐标,`generate_population`函数用于生成初始的鱼群,`calculate_fitness`函数用于计算每个鱼群的适应度,`move_towards`函数用于计算每个鱼的移动方向,`update_position`函数用于更新每个鱼的位置,`update_population`函数用于更新整个鱼群,`plot_tsp`函数用于可视化最优路径,`run_afsa`函数用于运行整个算法并返回最优路径的适应度。
你可以根据需要修改参数以及实现细节。