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Article Details

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.

Comparative Analysis of Evolutionary Algorithms for Multi-Objective Travelling Salesman Problem

Author 1: Nosheen Qamar
Author 2: Nadeem Akhtar
Author 3: Irfan Younas

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.090251

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 2, 2018.

  • Abstract and Keywords
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Abstract: The Evolutionary Computation has grown much in last few years. Inspired by biological evolution, this field is used to solve NP-hard optimization problems to come up with best solution. TSP is most popular and complex problem used to evaluate different algorithms. In this paper, we have conducted a comparative analysis between NSGA-II, NSGA-III, SPEA-2, MOEA/D and VEGA to find out which algorithm best suited for MOTSP problems. The results reveal that the MOEA/D performed better than other three algorithms in terms of more hypervolume, lower value of generational distance (GD), inverse generational distance (IGD) and adaptive epsilon. On the other hand, MOEA-D took more time than rest of the algorithms.

Keywords: Evolutionary computation; algorithms; NSGA-II; NSGA-III; MOEA-D; comparative analysis

Nosheen Qamar, Nadeem Akhtar and Irfan Younas, “Comparative Analysis of Evolutionary Algorithms for Multi-Objective Travelling Salesman Problem” International Journal of Advanced Computer Science and Applications(IJACSA), 9(2), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090251

@article{Qamar2018,
title = {Comparative Analysis of Evolutionary Algorithms for Multi-Objective Travelling Salesman Problem},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090251},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090251},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {2},
author = {Nosheen Qamar and Nadeem Akhtar and Irfan Younas}
}


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