From dd5fd385ab75b3edfaf016689bd6b4279e13aaf3 Mon Sep 17 00:00:00 2001 From: UmbertoJr Date: Mon, 2 Dec 2019 08:25:20 +0100 Subject: [PATCH] meta --- src/TSP_solver.py | 3 ++- src/meta_heuristics.py | 58 ++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 58 insertions(+), 3 deletions(-) diff --git a/src/TSP_solver.py b/src/TSP_solver.py index 74072cc..b89d927 100644 --- a/src/TSP_solver.py +++ b/src/TSP_solver.py @@ -20,7 +20,8 @@ class Solver_TSP: } available_improvements = {"2-opt": TwoOpt.loop2opt, - "2.5-opt": TwoDotFiveOpt.loop2dot5opt} + "2.5-opt": TwoDotFiveOpt.loop2dot5opt, + "simulated_annealing": Simulated_Annealing.sm} # , # "simulated_annealing": Simulated_Annealing, diff --git a/src/meta_heuristics.py b/src/meta_heuristics.py index 8e34a6e..ee000d1 100644 --- a/src/meta_heuristics.py +++ b/src/meta_heuristics.py @@ -1,7 +1,61 @@ +import numpy as np + +if 'AI' in os.getcwd(): + from src.utils import * +else: + from AI2019.src.utils import * + + class Simulated_Annealing: - def __call__(self): - pass + @staticmethod + def sa(solution, instance, constant_temperature=0.95, iterations_for_each_temp=100): + + # initial setup + temperature = instance.best_sol / np.sqrt(instance.nPoints) + current_sol = solution + current_len = compute_lenght(solution, instance.dist_matrix) + best_sol = solution + best_len = current_len + + # main loop + while temperature > 0.001: + for it in range(iterations_for_each_temp): + next_sol, delta_E = Simulated_Annealing.random_sol_from_neig(current_sol, instance) + if delta_E < 0: + current_sol = next_sol + current_len += delta_E + if current_len < best_len: + best_sol = current_sol + else: + r = np.random.uniform(0, 1) + if r < np.exp(- delta_E / temperature): + current_sol = next_sol + current_len += delta_E + + temperature *= constant_temperature + + return best_sol.tolist() + + @staticmethod + def random_sol_from_neig(solution, instance): + i, j = np.random.choice(np.arange(1, len(solution) - 1), 2, replace=False) + i, j = np.sort([i, j]) + return Simulated_Annealing.swap2opt(solution, i, j), Simulated_Annealing.gain() + + @staticmethod + def swap2opt(tsp_sequence, i, j): + new_tsp_sequence = np.copy(tsp_sequence) + new_tsp_sequence[i:j + 1] = np.flip(tsp_sequence[i:j + 1], axis=0) # flip or swap ? + return new_tsp_sequence + + @staticmethod + def gain(i, j, tsp_sequence, matrix_dist): + old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[ + tsp_sequence[j], tsp_sequence[j + 1]]) + changed_links_len = (matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[ + tsp_sequence[i], tsp_sequence[j + 1]]) + return - old_link_len + changed_links_len class Iterated_Local_Search: