From 89045a04c8c42504f765af10c7f069c683ef7c71 Mon Sep 17 00:00:00 2001
From: coolneng <akasroua@gmail.com>
Date: Tue, 22 Jun 2021 09:14:50 +0200
Subject: [PATCH] Adapt main module to the requirements

---
 src/local_search.py |  2 +-
 src/main.py         | 30 ++++++------------------------
 2 files changed, 7 insertions(+), 25 deletions(-)

diff --git a/src/local_search.py b/src/local_search.py
index 7ead8ad..ca65f9e 100644
--- a/src/local_search.py
+++ b/src/local_search.py
@@ -97,7 +97,7 @@ def evaluate_all_solutions(solutions, data, cores=4):
     return fitness
 
 
-def local_search(n, m, data, number_solutions=10):
+def multistart_local_search(n, m, data, number_solutions=10):
     initial_solutions = generate_initial_solutions(n, m, data, number_solutions)
     solutions = []
     for solution in initial_solutions:
diff --git a/src/main.py b/src/main.py
index 887484b..f4a64df 100755
--- a/src/main.py
+++ b/src/main.py
@@ -1,27 +1,14 @@
 from preprocessing import parse_file
-from genetic_algorithm import genetic_algorithm
-from memetic_algorithm import memetic_algorithm
+from local_search import multistart_local_search
+from simulated_annealing import simulated_annealing
 from time import time
 from argparse import ArgumentParser
 
 
 def execute_algorithm(args, n, m, data):
-    if args.algorithm == "genetic":
-        return genetic_algorithm(
-            n,
-            m,
-            data,
-            select_mode=args.selection,
-            crossover_mode=args.crossover,
-            max_iterations=100,
-        )
-    return memetic_algorithm(
-        n,
-        m,
-        data,
-        hybridation=args.hybridation,
-        max_iterations=100,
-    )
+    if args.algorithm == "annealing":
+        return simulated_annealing(n, m, data, max_iterations=100)
+    return multistart_local_search(n, m, data)
 
 
 def show_results(solution, time_delta):
@@ -36,12 +23,7 @@ def show_results(solution, time_delta):
 def parse_arguments():
     parser = ArgumentParser()
     parser.add_argument("file", help="dataset of choice")
-    subparsers = parser.add_subparsers(dest="algorithm")
-    parser_genetic = subparsers.add_parser("genetic")
-    parser_memetic = subparsers.add_parser("memetic")
-    parser_genetic.add_argument("crossover", choices=["uniform", "position"])
-    parser_genetic.add_argument("selection", choices=["generational", "stationary"])
-    parser_memetic.add_argument("hybridation", choices=["all", "random", "best"])
+    parser.add_argument("algorithm", choices=["annealing", "multisearch"])
     return parser.parse_args()