 chatGPT答： |
8v6.cn |
以下是一个基本的ACO算法的Python代码示例：
```
import random
class AntColonyOptimizer:
def __init__(self, num_ants, num_iterations, alpha=1.0, beta=2.0,
evaporation_rate=0.5):
self.num_ants = num_ants
self.num_iterations = num_iterations
self.alpha = alpha
self.beta = beta
self.evaporation_rate = evaporation_rate
def optimize(self, graph):
pheromone_levels = [[1.0 for j in range(len(graph))] for i in range(len(graph))]
best_path_cost = float('inf')
for iteration in range(self.num_iterations):
ant_paths = []
# Generate paths for each ant
for ant_index in range(self.num_ants):
path_cost, path_nodes = self.generate_ant_path(graph,
pheromone_levels)
if path_cost < best_path_cost:
best_path_cost = path_cost
ant_paths.append((path_nodes, path_cost))
# Update pheromone levels based on the paths taken by the ants
delta_pheromones_matrix =[[(1 - self.evaporation_rate) * pheromone_levels[i][j]
for j in range(len(graph))]
for i in range(len(graph))]
# Add pheromones to edges used by ants that found a good solution.
# The amount of added pheromones is proportional to the quality of the solution.
sorted_ant_paths_by_score_descendingly \
=[path_tuple[0]
for path_tuple
in sorted(ant_paths,key=lambda x:x[1])]
def generate_ant_path(self, graph, pheromone_levels):
start_node = random.randint(0, len(graph) - 1)
visited_nodes = set([start_node])
current_node = start_node
path_cost = 0.0
path_nodes = [start_node]
while len(visited_nodes) < len(graph):
# Calculate probabilities for each unvisited neighbor of the current node
unvisited_neighbors_probabilities = []
for neighbor in range(len(graph)):
if neighbor not in visited_nodes:
pheromone_level = pheromone_levels[current_node][neighbor]
distance_to_neighbor = graph[current_node][neighbor]
probability_of_selecting_neighbor \
=(pheromone_level ** self.alpha) * ((1.0 / distance_to_neighbor) ** self.beta)
unvisited_neighbors_probabilities.append((neighbor, probability_of_selecting_neighbor))
# Select the next node to visit based on the probabilities calculated above.
# This is done by using a weighted random choice.
total_probability_sum \
=[prob_tuple[1]
for prob_tuple
in unvisited_neighbors_probabilities].sum()
def update_pheromones(self, delta_pheromones_matrix):
# Example usage:
graph_data \
=[ [0, 2, 4],
[2, 0, 3],
[4, 3, 0]]
aco_optimizer=AntColonyOptimizer(num_ants=10,num_iterations=100,alpha=1,beta=2,evaporation_rate=.5)
aco_optimizer.optimize(graph_data)
```
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