First draft of exhaustive search.

hill_climbing.py

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+from typing import Tuple
+
+import numpy as np
+import numpy.typing as npt
+import copy
+
+from common import plot_plan, read_data
+
+original_perm = None
+
+def hill_climbing(distances: npt.NDArray) -> Tuple[float, npt.NDArray]:
+    size = len(distances)
+    perm = np.arange(size)
+    np.random.shuffle(perm)
+
+    print(perm)
+    global original_perm
+    original_perm = copy.deepcopy(perm)
+    current_route: float = np.sum([distances[perm[i - 1], perm[i]] for i in range(size)])
+    found_improvement = True
+    while found_improvement:
+        found_improvement = False
+        tmp_improvement: float = current_route
+        improvement_index: int = -1
+        for i in range(size):
+            perm[i - 1], perm[i] = perm[i], perm[i - 1]
+            tmp_route: float = np.sum([distances[perm[i - 1], perm[i]] for i in range(size)])
+            if tmp_route < tmp_improvement:
+                tmp_improvement = tmp_route
+                improvement_index = i
+                found_improvement = True
+
+            perm[i - 1], perm[i] = perm[i], perm[i - 1]
+
+        if found_improvement:
+            current_route = tmp_improvement
+            perm[improvement_index - 1], perm[improvement_index] = (
+                perm[improvement_index],
+                perm[improvement_index - 1],
+            )
+
+    print(perm)
+
+    return (current_route, perm)
+
+
+if __name__ == "__main__":
+    cities, data = read_data("./european_cities.csv")
+    distance, perm = hill_climbing(data[:10,:10])
+
+    plot_plan(list(map(lambda i: cities[i], list(perm))))
+    plot_plan(list(map(lambda i: cities[i], list(original_perm))))
+