Implement multi-start local search

src/local_search.py

@@ -1,5 +1,8 @@
 from numpy.random import choice, seed, randint
 from pandas import DataFrame
+from multiprocessing import Pool
+from functools import partial
+from itertools import combinations
 
 
 def get_row_distance(source, destination, data):
@@ -22,7 +25,7 @@ def compute_distance(element, solution, data):
     return accumulator
 
 
-def get_first_random_solution(n, m, data):
+def get_first_random_solution(placeholder, n, m, data):
     solution = DataFrame(columns=["point", "distance"])
     seed(42)
     solution["point"] = choice(n, size=m, replace=False)
@@ -67,9 +70,41 @@ def explore_neighbourhood(element, n, data, max_iterations=100000):
     return neighbour
 
 
-def local_search(n, m, data):
-    first_solution = get_first_random_solution(n, m, data)
-    best_solution = explore_neighbourhood(
-        element=first_solution, n=n, data=data, max_iterations=100
+def evaluate_solution(solution, data):
+    fitness = 0
+    comb = combinations(solution.index, r=2)
+    for index in list(comb):
+        elements = solution.loc[index, :]
+        fitness += get_row_distance(
+            source=elements["point"].head(n=1).values[0],
+            destination=elements["point"].tail(n=1).values[0],
+            data=data,
         )
-    return best_solution
+    return fitness
+
+
+def generate_initial_solutions(n, m, data, number_solutions, cores=4):
+    generation_func = partial(get_first_random_solution, n=n, m=m, data=data)
+    with Pool(cores) as pool:
+        initial_solutions = pool.map(generation_func, range(number_solutions))
+    return initial_solutions
+
+
+def evaluate_all_solutions(solutions, data, cores=4):
+    generation_func = partial(evaluate_solution, data=data)
+    with Pool(cores) as pool:
+        fitness = pool.map(generation_func, solutions)
+    return fitness
+
+
+def local_search(n, m, data, number_solutions=10):
+    initial_solutions = generate_initial_solutions(n, m, data, number_solutions)
+    solutions = []
+    for solution in initial_solutions:
+        local_best_solution = explore_neighbourhood(
+            element=solution, n=n, data=data, max_iterations=100
+        )
+        solutions.append(local_best_solution)
+    fitness = evaluate_all_solutions(solutions, data)
+    best_index = fitness.index(max(fitness))
+    return solutions[best_index]