Title: MGA-TSP: modernised genetic algorithm for the travelling salesman problem

Authors: Ra'ed M. Al-Khatib; Mohammed Azmi Al-Betar; Mohammed A. Awadallah; Khalid M.O. Nahar; Mohammed M. Abu Shquier; Ahmad M. Manasrah; Ahmad Bany Doumi

Addresses: Department of Computer Sciences, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid-21163, Jordan ' Department of Information Technology, Al-Huson University College, Al-Balqa Applied University (BAU), P.O. Box 50, Al-Huson, Irbid, Jordan ' Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine ' Department of Computer Sciences, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid-21163, Jordan ' Faculty of Computer Science and Information Technology, Jerash University, Jerash, Jordan ' Network and Information Security Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid-21163, Jordan ' Department of Computer Sciences, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid-21163, Jordan

Abstract: This paper proposes a new enhanced algorithm called modernised genetic algorithm for solving the travelling salesman problem (MGA-TSP). Recently, the most successful evolutionary algorithm used for TSP problem, is GA algorithm. The main obstacles for GA is building its initial population. Therefore, in this paper, three neighbourhood structures (inverse, insert, and swap) along with 2-opt is utilised to build strong initial population. Additionally, the main operators (i.e., crossover and mutation) of GA during the generation process are also enhanced for TSP. Therefore, powerful crossover operator called EAX is utilised in the proposed MGA-TSP to enhance its convergence. For validation purpose, we used TSP datasets, range from 150 to 33,810 cities. Initially, the impact of each neighbouring structure on the performance of MGA-TSP is studied. In conclusion, MGA-TSP achieved the best results. For comparative evaluation. MGA-TSP is able to outperform six comparative methods in almost all TSP instances used.

Keywords: travelling salesman problem; optimisation; genetic algorithm; neighbouring operators.

DOI: 10.1504/IJRIS.2019.102541

International Journal of Reasoning-based Intelligent Systems, 2019 Vol.11 No.3, pp.215 - 226

Received: 01 Jun 2018
Accepted: 14 Jul 2018
Published online: 30 Sep 2019 *

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