tabu search on example

main.py

@@ -0,0 +1,71 @@
+#/bin/python
+
+# edges = [[None, 6,    9,    18,   None],
+#          [6,    None, 2,    None, 8],
+#          [9,    2,    None, 0,    None],
+#          [18,   None, 0,    None, 12],
+#          [None, 8,    None, 12,   None]]
+
+costs = {"x1":6, "x2":9, "x3":18, "x4":2, "x5":0, "x6":8, "x7":12}
+solution = {"x1":1, "x2":0, "x3":0, "x4":1, "x5":1, "x6":1, "x7":0}
+
+def check_constraint(solution):
+    penalty = 0
+    if solution["x1"] and not solution["x3"]:
+        penalty += 50
+    if solution["x1"] + solution["x2"] + solution["x6"] > 1:
+        penalty +=50
+    return penalty
+
+def check_validity(solution):
+    return (sum(solution.values()) == 4) and not ((solution["x1"] and solution["x2"] and solution["x4"]) or (solution["x2"] and solution["x3"] and solution["x5"]) or (solution["x4"] and solution["x5"] and solution["x7"] and solution["x6"]) or (solution["x1"] and solution["x3"] and solution["x6"] and solution["x7"]) or (solution["x1"] and solution["x3"] and solution["x4"] and solution["x5"]))
+
+def neighbors(solution):
+    neighbors = list()
+    dict_0 = {k: v for k, v in solution.items() if v == 0}
+    dict_1 = {k: v for k, v in solution.items() if v == 1}
+    for i in dict_0.keys():
+        for j in dict_1.keys():
+            copy = solution.copy()
+            copy[i]=1
+            copy[j]=0
+            neighbors.append(copy)
+    return neighbors
+
+def calculate_cost(solution):
+    return check_constraint(solution) + sum(costs[key] for key in solution if solution[key] == 1)
+
+def tabu_search(initial_solution, n_max):
+    solution = initial_solution
+    best = initial_solution
+    cost = calculate_cost(initial_solution)
+    cost_best = cost
+    tabu = [solution,]
+    n = 0
+    while n <= n_max:
+        neighbors_list = []
+        for s in neighbors(solution):
+            if check_validity(s) and s not in tabu:
+                neighbors_list.append((s, calculate_cost(s)))
+        new = min(neighbors_list, key=lambda x: x[1])
+        if cost - new[1] >= 0:
+            n = 0
+            solution = new[0]
+            cost = new[1]
+            if cost_best - new[1] >= 0:
+                best = new[0]
+                cost_best = new[1]
+        else:
+            solution = new[0]
+            cost = new[1]
+            n += 1
+        tabu.append(solution)
+
+    return (best, cost_best)
+
+print("when not degrading the current solution:")
+print(tabu_search(solution, 0))
+print("tabu search accepting 1 degradation:")
+print(tabu_search(solution, 1))
+print("tabu search accepting 2 degradations:")
+print(tabu_search(solution, 2))