python ACO库的代码
时间: 2023-12-16 12:06:43 浏览: 36
以下是一个简单的 Python ACO 库的示例代码:
```
import random
class ACO:
def __init__(self, colony_size, iterations, alpha, beta, rho, q):
self.colony_size = colony_size
self.iterations = iterations
self.alpha = alpha
self.beta = beta
self.rho = rho
self.q = q
def solve(self, problem):
self.problem = problem
self.colony = [Ant(self) for i in range(self.colony_size)]
self.best_solution = None
self.best_distance = float("inf")
for i in range(self.iterations):
for ant in self.colony:
ant.reset()
for j in range(len(problem.nodes) - 1):
ant.choose_next()
ant.total_distance += problem.distance(ant.tabu[-1], ant.tabu[0])
if ant.total_distance < self.best_distance:
self.best_distance = ant.total_distance
self.best_solution = ant.tabu
for node in problem.nodes:
for ant in self.colony:
if node in ant.tabu:
node.pheromone *= (1 - self.rho)
if ant.total_distance < self.best_distance:
node.pheromone += self.q / ant.total_distance
else:
node.pheromone += self.q / self.best_distance
class Ant:
def __init__(self, aco):
self.aco = aco
self.total_distance = 0.0
self.tabu = []
self.possible_nodes = [i for i in range(len(aco.problem.nodes))]
self.current_node = random.randint(0, len(aco.problem.nodes) - 1)
self.tabu.append(self.current_node)
self.possible_nodes.remove(self.current_node)
def reset(self):
self.total_distance = 0.0
self.tabu = []
self.possible_nodes = [i for i in range(len(self.aco.problem.nodes))]
self.current_node = random.randint(0, len(self.aco.problem.nodes) - 1)
self.tabu.append(self.current_node)
self.possible_nodes.remove(self.current_node)
def choose_next(self):
denominator = 0.0
for node in self.possible_nodes:
denominator += self.aco.problem.heuristic(self.current_node, node, self.aco.alpha, self.aco.beta)
if denominator == 0.0:
return
probabilities = []
for node in self.possible_nodes:
numerator = self.aco.problem.heuristic(self.current_node, node, self.aco.alpha, self.aco.beta)
probability = numerator / denominator
probabilities.append((node, probability))
selected_node = self.select_next(probabilities)
self.tabu.append(selected_node)
self.possible_nodes.remove(selected_node)
self.total_distance += self.aco.problem.distance(self.current_node, selected_node)
self.current_node = selected_node
def select_next(self, probabilities):
r = random.uniform(0, 1)
for (node, probability) in probabilities:
if r <= probability:
return node
r -= probability
return probabilities[-1][0]
class TSP:
def __init__(self, nodes):
self.nodes = nodes
self.pheromone = [[1.0 / len(nodes) for j in range(len(nodes))] for i in range(len(nodes))]
def distance(self, i, j):
n1 = self.nodes[i]
n2 = self.nodes[j]
return ((n1[0] - n2[0]) ** 2 + (n1[1] - n2[1]) ** 2) ** 0.5
def heuristic(self, i, j, alpha, beta):
return ((self.pheromone[i][j] ** alpha) * ((1.0 / self.distance(i, j)) ** beta))
```
这个库实现的是一个简单的蚁群算法,用于解决旅行商问题(TSP)。在这个库中,ACO 类是主要的算法类,Ant 类是蚂蚁类,TSP 类是用于表示旅行商问题的类。
使用这个库时,你需要创建一个 TSP 实例,传入一个节点列表。然后创建一个 ACO 实例,设置一些算法参数,然后调用 solve 方法来解决问题。
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