46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
import math, random
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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)
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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 SimulatedAnnealing:
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def __init__(self, cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001):
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self.cities = cities
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self.temperature = temperature
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self.cooling_rate = cooling_rate
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self.temperature_ok = temperature_ok
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self.distances = []
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self.temperatures = []
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def run(self):
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interration = 0
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current_solution = self.cities.copy()
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best_solution = self.cities.copy()
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while self.temperature > self.temperature_ok:
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new_solution = current_solution.copy()
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# Swap two cities in the route
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i = random.randint(0, len(new_solution) - 1)
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j = random.randint(0, len(new_solution) - 1)
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new_solution[i], new_solution[j] = new_solution[j], new_solution[i]
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# Calculate the acceptance probability
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current_energy = total_distance(current_solution)
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new_energy = total_distance(new_solution)
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delta = new_energy - current_energy
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if delta < 0 or random.random() < math.exp(-delta / self.temperature):
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current_solution = new_solution
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if total_distance(current_solution) < total_distance(best_solution):
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best_solution = current_solution
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if interration % 10 == 0:
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self.distances.append(total_distance(current_solution))
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# Cool down
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self.temperature *= self.cooling_rate
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interration += 1
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# Print every 1000 iterations
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if interration % 10 == 0:
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print("Iteration", interration, "with distance", total_distance(current_solution))
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return best_solution, self.distances |