Correct wrong naming.

Author: skytreader

python/stochastic/variable_local_search.py

@@ -0,0 +1,73 @@
+#! /usr/bin/env python3
+
+from .common import cost, random_permutation
+
+import random
+
+"""
+2.7
+
+Variable Neighborhood Search involves iterative exploration of larger and larger
+neighborhoods for a given local optima until an improvement is located after
+which time the search across expanding neighborhoods is repeated. The strategy
+is motivated by three principles: 1) a local minimum for one neighborhood
+structure may not be a local minimum for another neighborhood structure, 2) a
+global minimum is local minimum for all possible neighborhood structures, and
+3) local minima are relatively close to global minima for many problem classes.
+"""
+
+def stochastic_two_opt(perm):
+    randlimit = len(perm) - 1
+    c1, c2 = random.randint(0, randlimit), random.randint(0, randlimit)
+    exclude = [c1]
+    exclude.append(randlimit if c1 == 0 else c1 -1)
+    exclude.append(0 if c1 == randlimit else c1 + 1)
+
+    while c2 in exclude:
+        c2 = random.randint(0, randlimit)
+
+    c1, c2 = c2, c1 if c2 < c1 else None
+    perm[c1:c2] = perm[c1:c2][::-1]
+    return perm
+
+def local_search(best, cities, max_no_improv, neighborhood_size):
+    count = 0
+
+    while count < max_no_improv:
+        candidate = {}
+        candidate["vector"] = [v for v in best["vector"]]
+
+        for _ in range(neighborhood_size):
+            stochastic_two_opt(candidate["vector"])
+
+        candidate["cost"] = cost(candidate["vector"], cities)
+
+        if candidate["cost"] < best["cost"]:
+            count, best = 0, candidate
+        else:
+            count += 1
+
+    return best
+
+def search(cities, neigborhoods, max_no_improv, max_no_improv_ls):
+    best = {}
+    best["vector"] = random_permutation(cities)
+    best["cost"] = cost(best["vector"], cities)
+    iter_, count = 0, 0
+
+    while count < max_no_improv:
+        for neigh in negihborhoods:
+            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 = 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")