TY - GEN
T1 - A dragonfly algorithm for solving traveling salesman problem
AU - Hammouri, Abdelaziz I.
AU - Samra, Enas Tawfiq Abu
AU - Al-Betar, Mohammed Azmi
AU - Khalil, Raid M.
AU - Alasmer, Ziad
AU - Kanan, Monther
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/4/8
Y1 - 2019/4/8
N2 - Traveling Salesman Problem (TSP) is considered as nondeterministic polynomial time hard problem. In the TSP, a salesman should visit a set of cities, and the distances between all pairs of cities are known in advance. The salesman has to find the shortest tour for visiting all cities exactly once and returns back to the starting city. Various methods have been used to tackle TSP, the most commonly employed methods are meta-heuristic algorithms. In this paper, TSP has been tackled by employing a newly created meta-heuristic algorithm, named Dragonfly Algorithm (DA), on well-known datasets (TSPLIB). The idea of DA has been inspired from swarm intelligence. To assess the quality of the proposed approach, it will be compared with other meta-heuristic methods that are available in the literature using the same datasets. The final results showed that the proposed DA-based TSP problem method is able to efficiently address the TSP and it produces competitively comparable results against others produces by well-regards comparative methods.
AB - Traveling Salesman Problem (TSP) is considered as nondeterministic polynomial time hard problem. In the TSP, a salesman should visit a set of cities, and the distances between all pairs of cities are known in advance. The salesman has to find the shortest tour for visiting all cities exactly once and returns back to the starting city. Various methods have been used to tackle TSP, the most commonly employed methods are meta-heuristic algorithms. In this paper, TSP has been tackled by employing a newly created meta-heuristic algorithm, named Dragonfly Algorithm (DA), on well-known datasets (TSPLIB). The idea of DA has been inspired from swarm intelligence. To assess the quality of the proposed approach, it will be compared with other meta-heuristic methods that are available in the literature using the same datasets. The final results showed that the proposed DA-based TSP problem method is able to efficiently address the TSP and it produces competitively comparable results against others produces by well-regards comparative methods.
KW - Dragonfly Algorithm
KW - NP-hard problem
KW - Travelling salesman problem
KW - meta-heuristic
KW - optimization
UR - https://www.scopus.com/pages/publications/85065046375
U2 - 10.1109/ICCSCE.2018.8684963
DO - 10.1109/ICCSCE.2018.8684963
M3 - Conference contribution
AN - SCOPUS:85065046375
T3 - Proceedings - 8th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2018
SP - 136
EP - 141
BT - Proceedings - 8th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2018
Y2 - 23 November 2018 through 25 November 2018
ER -