66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
import numpy as np
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import os
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if 'AI' in os.getcwd():
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from src.utils import *
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else:
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from AI2019.src.utils import *
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class Simulated_Annealing:
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@staticmethod
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def sa(solution, instance, constant_temperature=0.95, iterations_for_each_temp=100):
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# initial setup
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temperature = instance.best_sol / np.sqrt(instance.nPoints)
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current_sol = np.array(solution)
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current_len = compute_lenght(solution, instance.dist_matrix)
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best_sol = np.array(solution)
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best_len = current_len
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# main loop
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while temperature > 0.001:
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for it in range(iterations_for_each_temp):
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next_sol, delta_E = Simulated_Annealing.random_sol_from_neig(current_sol, instance)
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if delta_E < 0:
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current_sol = next_sol
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current_len += delta_E
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if current_len < best_len:
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best_sol = current_sol
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else:
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r = np.random.uniform(0, 1)
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if r < np.exp(- delta_E / temperature):
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current_sol = next_sol
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current_len += delta_E
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temperature *= constant_temperature
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return best_sol.tolist()
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@staticmethod
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def random_sol_from_neig(solution, instance):
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i, j = np.random.choice(np.arange(1, len(solution) - 1), 2, replace=False)
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i, j = np.sort([i, j])
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return Simulated_Annealing.swap2opt(solution, i, j), Simulated_Annealing.gain(i, j, solution,
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instance.dist_matrix)
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@staticmethod
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def swap2opt(tsp_sequence, i, j):
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new_tsp_sequence = np.copy(tsp_sequence)
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new_tsp_sequence[i:j + 1] = np.flip(tsp_sequence[i:j + 1], axis=0) # flip or swap ?
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return new_tsp_sequence
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@staticmethod
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def gain(i, j, tsp_sequence, matrix_dist):
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old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[
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tsp_sequence[j], tsp_sequence[j + 1]])
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changed_links_len = (matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[
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tsp_sequence[i], tsp_sequence[j + 1]])
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return - old_link_len + changed_links_len
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class Iterated_Local_Search:
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def __call__(self):
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pass
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