51 lines
2.3 KiB
Python
51 lines
2.3 KiB
Python
import math, random, time, numpy as np
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def distance(city1, city2):
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return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2) + 1e-10
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def total_distance(cities):
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return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
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class AntColony:
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def __init__(self, cities, n_ants, alpha=1, beta=2, evaporation=0.5, intensification=2, max_time=0.1):
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self.cities = cities
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self.n = len(cities)
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self.n_ants = n_ants
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self.alpha = alpha
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self.beta = beta
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self.evaporation = evaporation
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self.intensification = intensification
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self.max_time = max_time
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self.pheromones = [[1 / self.n for _ in range(self.n)] for __ in range(self.n)]
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def choose_next_city(self, ant):
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unvisited_cities = [i for i in range(self.n) if i not in ant]
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probabilities = [self.pheromones[ant[-1]][i] ** self.alpha * ((1 / distance(self.cities[ant[-1]], self.cities[i])) ** self.beta) for i in unvisited_cities]
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total = sum(probabilities)
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if total == 0:
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probabilities = [1 / len(unvisited_cities) for _ in unvisited_cities]
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else:
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probabilities = [p / total for p in probabilities]
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return np.random.choice(unvisited_cities, p=probabilities)
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def update_pheromones(self, ant):
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pheromones_delta = self.intensification / total_distance([self.cities[i] for i in ant])
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for i in range(len(ant) - 1):
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self.pheromones[ant[i]][ant[i+1]] += pheromones_delta
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def run(self):
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best_ant = []
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best_distance = float('inf')
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start_time = time.time()
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while time.time() - start_time < self.max_time:
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ants = [[random.randint(0, self.n - 1)] for _ in range(self.n_ants)]
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for ant in ants:
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for _ in range(self.n - 1):
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ant.append(self.choose_next_city(ant))
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ant_distance = total_distance([self.cities[i] for i in ant])
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if ant_distance < best_distance:
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best_distance = ant_distance
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best_ant = ant.copy()
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self.update_pheromones(ant)
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self.pheromones = [[(1 - self.evaporation) * p for p in row] for row in self.pheromones]
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return [self.cities[i] for i in best_ant] |