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DOI: 10.14569/IJACSA.2020.0110768
PDF

Genetic Algorithms for the Multiple Travelling Salesman Problem

Author 1: Maha Ata Al-Furhud
Author 2: Zakir Hussain Ahmed

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: We consider the multiple travelling salesman Problem (MTSP) that is one of the generalization of the travelling salesman problem (TSP). For solving this problem genetic algorithms (GAs) based on numerous crossover operators have been described in the literature. Choosing effective crossover operator can give effective GA. Generally, the crossover operators that are developed for the TSP are applied to the MTSP. We propose to develop simple and effective GAs using sequential constructive crossover (SCX), adaptive SCX, greedy SCX, reverse greedy SCX and comprehensive SCX for solving the MTSP. The effectiveness of the crossover operators is demonstrated by comparing among them and with another crossover operator on some instances from TSPLIB of various sizes with different number of salesmen. The experimental study shows the promising results by the crossover operators, especially CSCX, for the MTSP.

Keywords: Multiple travelling salesman problem; NP-hard; genetic algorithm; sequential constructive crossover; adaptive; greedy; comprehensive

Maha Ata Al-Furhud and Zakir Hussain Ahmed, “Genetic Algorithms for the Multiple Travelling Salesman Problem” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110768

@article{Al-Furhud2020,
title = {Genetic Algorithms for the Multiple Travelling Salesman Problem},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110768},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110768},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Maha Ata Al-Furhud and Zakir Hussain Ahmed}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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