src/processing.py

@@ -64,32 +64,25 @@ def get_first_random_solution(m, data):
     return data.iloc[random_indexes]
 
 
-def replace_worst_element(previous, data):
+def get_random_solution(previous, data):
     solution = previous.copy()
     worst_index = previous["distance"].astype(float).idxmin()
+    worst_element = previous["distance"].loc[worst_index]
     random_candidate = data.loc[randint(low=0, high=len(data.index))]
-    solution.loc[worst_index] = random_candidate
-    return solution
-
-
-def get_random_solution(previous, data):
-    solution = replace_worst_element(previous, data)
-    while solution["distance"].sum() <= previous["distance"].sum():
-        if solution.equals(previous):
-            break
-        solution = replace_worst_element(previous=solution, data=data)
-    return solution
+    while solution["distance"].loc[worst_index] <= worst_element:
+        if random_candidate["distance"] not in solution["distance"].values:
+            solution.loc[worst_index] = random_candidate
+        else:
+            return solution, True
+    return solution, False
 
 
 def explore_neighbourhood(element, data, max_iterations=100000):
-    neighbourhood = []
-    neighbourhood.append(element)
+    neighbour = DataFrame()
     for _ in range(max_iterations):
-        previous_solution = neighbourhood[-1]
-        neighbour = get_random_solution(previous=previous_solution, data=data)
-        if neighbour.equals(previous_solution):
+        neighbour, stop_condition = get_random_solution(element, data)
+        if stop_condition:
             break
-        neighbourhood.append(neighbour)
     return neighbour