meta

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,

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: