Refactor some stuff. Finish Variable Neighborhood Search.

Although we need to statistically analyze the Python and Ruby versions, ASAP
since there seems to be a considerable discrepancy between the Python and Ruby
versions of Variable Neighborhood Search.

Author: skytreader

python/stochastic/common.py

@@ -39,3 +39,14 @@ def path_cost(permutation, cities):
         distance += euc_2d(cities[permutation[i]], cities[c2])
 
     return distance
+
+# Problem configuration
+berlin52 = [[565,575],[25,185],[345,750],[945,685],[845,655],
+[880,660],[25,230],[525,1000],[580,1175],[650,1130],[1605,620],
+[1220,580],[1465,200],[1530,5],[845,680],[725,370],[145,665],
+[415,635],[510,875],[560,365],[300,465],[520,585],[480,415],
+[835,625],[975,580],[1215,245],[1320,315],[1250,400],[660,180],
+[410,250],[420,555],[575,665],[1150,1160],[700,580],[685,595],
+[685,610],[770,610],[795,645],[720,635],[760,650],[475,960],
+[95,260],[875,920],[700,500],[555,815],[830,485],[1170,65],
+[830,610],[605,625],[595,360],[1340,725],[1740,245]]

python/stochastic/iterated_local_search.py

@@ -102,16 +102,7 @@ def search(cities, max_iterations, max_no_improv):
     return best
 
 if __name__ == "__main__":
-    # Problem configuration
-    berlin52 = [[565,575],[25,185],[345,750],[945,685],[845,655],
-    [880,660],[25,230],[525,1000],[580,1175],[650,1130],[1605,620],
-    [1220,580],[1465,200],[1530,5],[845,680],[725,370],[145,665],
-    [415,635],[510,875],[560,365],[300,465],[520,585],[480,415],
-    [835,625],[975,580],[1215,245],[1320,315],[1250,400],[660,180],
-    [410,250],[420,555],[575,665],[1150,1160],[700,580],[685,595],
-    [685,610],[770,610],[795,645],[720,635],[760,650],[475,960],
-    [95,260],[875,920],[700,500],[555,815],[830,485],[1170,65],
-    [830,610],[605,625],[595,360],[1340,725],[1740,245]]
+    from .common import berlin52
 
     # Algorithm configuration
     max_iterations = 100

python/stochastic/variable_neighborhood_search.py

@@ -1,6 +1,6 @@
 #! /usr/bin/env python3
 
-from .common import cost, random_permutation
+from .common import path_cost, random_permutation
 
 import random
 
@@ -40,7 +40,7 @@ def local_search(best, cities, max_no_improv, neighborhood_size):
         for _ in range(neighborhood_size):
             stochastic_two_opt(candidate["vector"])
 
-        candidate["cost"] = cost(candidate["vector"], cities)
+        candidate["cost"] = path_cost(candidate["vector"], cities)
 
         if candidate["cost"] < best["cost"]:
             count, best = 0, candidate
@@ -52,22 +52,35 @@ def local_search(best, cities, max_no_improv, neighborhood_size):
 def search(cities, neigborhoods, max_no_improv, max_no_improv_ls):
     best = {}
     best["vector"] = random_permutation(cities)
-    best["cost"] = cost(best["vector"], cities)
+    best["cost"] = path_cost(best["vector"], cities)
     iter_, count = 0, 0
 
     while count < max_no_improv:
-        for neigh in negihborhoods:
+        for neigh in neighborhoods:
             candidate = {}
             candidate["vector"] = [v for v in best["vector"]]
 
             for _ in range(neigh):
                 stochastic_two_opt(candidate["vector"])
 
-            candidate["cost"] - cost(candidate["vector"], cities)
+            candidate["cost"] = path_cost(candidate["vector"], cities)
             candidate = local_search(candidate, cities, max_no_improv_ls, neigh)
             print("> iteration #%s, neigh=%s, best=%s" % (iter_ + 1, neigh, best["cost"]))
             iter_ += 1
 
             if candidate["cost"] < best["cost"]:
                 best, count = candidate, 0
                 print("New best, restarting neighborhood search")
+                break
+            else:
+                count += 1
+
+    return best
+
+if __name__ == "__main__":
+    from .common import berlin52
+    max_no_improv = 50
+    max_no_improv_ls = 70
+    neighborhoods = list(range(20))
+    best = search(berlin52, neighborhoods, max_no_improv, max_no_improv_ls)
+    print("Done. Best Solution: c=%s, v=%s" % (best["cost"], best["vector"]))