From 097ed9b52a1396e021abf3090de78afbc9915372 Mon Sep 17 00:00:00 2001
From: coolneng <akasroua@gmail.com>
Date: Mon, 19 Apr 2021 18:25:53 +0200
Subject: [PATCH] Remove redundant previous solution checks

---
 src/local_search.py | 26 +++++---------------------
 1 file changed, 5 insertions(+), 21 deletions(-)

diff --git a/src/local_search.py b/src/local_search.py
index cd64486..bf11982 100644
--- a/src/local_search.py
+++ b/src/local_search.py
@@ -17,28 +17,12 @@ def replace_worst_element(previous, data):
     return solution, worst_index
 
 
-def choose_best_solution(previous, current, index):
-    if previous.loc[index].distance >= current.loc[index].distance:
-        return previous
-    return current
-
-
 def get_random_solution(previous, data):
-    candidates = []
-    candidates.append(previous)
     solution, worst_index = replace_worst_element(previous, data)
     previous_worst_distance = previous["distance"].loc[worst_index]
-    last_solution = candidates[-1]
-    while last_solution.distance.loc[worst_index] <= previous_worst_distance:
+    while solution.distance.loc[worst_index] <= previous_worst_distance:
         solution, _ = replace_worst_element(previous=solution, data=data)
-        if solution.equals(last_solution):
-            best_solution = choose_best_solution(
-                previous=previous, current=solution, index=worst_index
-            )
-            return best_solution
-        candidates.append(solution)
-        last_solution = candidates[-1]
-    return last_solution
+    return solution
 
 
 def explore_neighbourhood(element, data, max_iterations=100000):
@@ -48,13 +32,13 @@ def explore_neighbourhood(element, data, max_iterations=100000):
         print(f"Iteration {i}")
         previous_solution = neighbourhood[-1]
         neighbour = get_random_solution(previous=previous_solution, data=data)
-        if neighbour.equals(previous_solution):
-            break
         neighbourhood.append(neighbour)
     return neighbour
 
 
 def local_search(m, data):
     first_solution = get_first_random_solution(m=m, data=data)
-    best_solution = explore_neighbourhood(element=first_solution, data=data)
+    best_solution = explore_neighbourhood(
+        element=first_solution, data=data, max_iterations=100
+    )
     return best_solution