Using 2-Opt based evolution strategy for travelling salesman problem
Harmony search algorithm that matches the (µ+1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette whee...
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Balikesir University
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doaj-260f441d0fda4fa8aae033f4974110772020-11-24T23:24:13ZengBalikesir UniversityAn International Journal of Optimization and Control: Theories & Applications 2146-09572146-57032016-03-016210311310.11121/ijocta.01.2016.0026881Using 2-Opt based evolution strategy for travelling salesman problemKenan KaragulErdal AydemirSezai TokatHarmony search algorithm that matches the (µ+1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions.http://ijocta.balikesir.edu.tr/index.php/files/article/view/268Travelling salesman problemsTSPharmony searchHS(µ+1) evolution strategy2-OptTSPLIB. |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kenan Karagul Erdal Aydemir Sezai Tokat |
spellingShingle |
Kenan Karagul Erdal Aydemir Sezai Tokat Using 2-Opt based evolution strategy for travelling salesman problem An International Journal of Optimization and Control: Theories & Applications Travelling salesman problems TSP harmony search HS (µ+1) evolution strategy 2-Opt TSPLIB. |
author_facet |
Kenan Karagul Erdal Aydemir Sezai Tokat |
author_sort |
Kenan Karagul |
title |
Using 2-Opt based evolution strategy for travelling salesman problem |
title_short |
Using 2-Opt based evolution strategy for travelling salesman problem |
title_full |
Using 2-Opt based evolution strategy for travelling salesman problem |
title_fullStr |
Using 2-Opt based evolution strategy for travelling salesman problem |
title_full_unstemmed |
Using 2-Opt based evolution strategy for travelling salesman problem |
title_sort |
using 2-opt based evolution strategy for travelling salesman problem |
publisher |
Balikesir University |
series |
An International Journal of Optimization and Control: Theories & Applications |
issn |
2146-0957 2146-5703 |
publishDate |
2016-03-01 |
description |
Harmony search algorithm that matches the (µ+1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions. |
topic |
Travelling salesman problems TSP harmony search HS (µ+1) evolution strategy 2-Opt TSPLIB. |
url |
http://ijocta.balikesir.edu.tr/index.php/files/article/view/268 |
work_keys_str_mv |
AT kenankaragul using2optbasedevolutionstrategyfortravellingsalesmanproblem AT erdalaydemir using2optbasedevolutionstrategyfortravellingsalesmanproblem AT sezaitokat using2optbasedevolutionstrategyfortravellingsalesmanproblem |
_version_ |
1725561300813086720 |