Application Methods of Ant Colony Algorithm

Elnaz Shafigh Fard; Khalil Monfaredi; Mohammad H. Nadimi

doi:doi:10.11648/j.ajsea.20140302.11

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Application Methods of Ant Colony Algorithm

Elnaz Shafigh Fard, Khalil Monfaredi, Mohammad H. Nadimi

Published in American Journal of Software Engineering and Applications (Volume 3, Issue 2)

Received: 11 May 2014 Accepted: 11 June 2014 Published: 30 June 2014

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Abstract

As one of the most prestigious and beneficial methods of artificial intelligence, ant colony takes the advantage of communal behavior of ants in nature for solving optimization problems in various fields. However, this useful algorithm requires extensive and repetitious computation, as a result, the processing duration of the present algorithm seems to be one of the most serious challenges about it. In order to solve optimization problems in which duration is very important, this paper attempts to review the previously applied methods and consider the advantages and the disadvantages of each method through highlighting the problems algorithm designers encounter.

Published in	American Journal of Software Engineering and Applications (Volume 3, Issue 2)
DOI	10.11648/j.ajsea.20140302.11
Page(s)	12-20
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Ant Colony, Optimization, Process Duration, Artificial Intelligence, Nature

References

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APA Style

Elnaz Shafigh Fard, Khalil Monfaredi, Mohammad H. Nadimi. (2014). Application Methods of Ant Colony Algorithm. American Journal of Software Engineering and Applications, 3(2), 12-20. https://doi.org/10.11648/j.ajsea.20140302.11

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ACS Style

Elnaz Shafigh Fard; Khalil Monfaredi; Mohammad H. Nadimi. Application Methods of Ant Colony Algorithm. Am. J. Softw. Eng. Appl. 2014, 3(2), 12-20. doi: 10.11648/j.ajsea.20140302.11

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AMA Style

Elnaz Shafigh Fard, Khalil Monfaredi, Mohammad H. Nadimi. Application Methods of Ant Colony Algorithm. Am J Softw Eng Appl. 2014;3(2):12-20. doi: 10.11648/j.ajsea.20140302.11

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@article{10.11648/j.ajsea.20140302.11,
  author = {Elnaz Shafigh Fard and Khalil Monfaredi and Mohammad H. Nadimi},
  title = {Application Methods of Ant Colony Algorithm},
  journal = {American Journal of Software Engineering and Applications},
  volume = {3},
  number = {2},
  pages = {12-20},
  doi = {10.11648/j.ajsea.20140302.11},
  url = {https://doi.org/10.11648/j.ajsea.20140302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20140302.11},
  abstract = {As one of the most prestigious and beneficial methods of artificial intelligence, ant colony takes the advantage of communal behavior of ants in nature for solving optimization problems in various fields. However, this useful algorithm requires extensive and repetitious computation, as a result, the processing duration of the present algorithm seems to be one of the most serious challenges about it.  In order to solve optimization problems in which duration is very important, this paper attempts to review the previously applied methods and consider the advantages and the disadvantages of each method through highlighting the problems algorithm designers encounter.},
 year = {2014}
}

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TY  - JOUR
T1  - Application Methods of Ant Colony Algorithm
AU  - Elnaz Shafigh Fard
AU  - Khalil Monfaredi
AU  - Mohammad H. Nadimi
Y1  - 2014/06/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ajsea.20140302.11
DO  - 10.11648/j.ajsea.20140302.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 12
EP  - 20
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20140302.11
AB  - As one of the most prestigious and beneficial methods of artificial intelligence, ant colony takes the advantage of communal behavior of ants in nature for solving optimization problems in various fields. However, this useful algorithm requires extensive and repetitious computation, as a result, the processing duration of the present algorithm seems to be one of the most serious challenges about it.  In order to solve optimization problems in which duration is very important, this paper attempts to review the previously applied methods and consider the advantages and the disadvantages of each method through highlighting the problems algorithm designers encounter.
VL  - 3
IS  - 2
ER  -

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Author Information

Elnaz Shafigh Fard

Faculty of Computer Engineering, Najafabad branch, Islamic Azad University, Isfahan, Iran
Khalil Monfaredi

Engineering Faculty, Department of Electrical and Electronic Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Mohammad H. Nadimi

Faculty of Computer Engineering, Najafabad branch, Islamic Azad University, Isfahan, Iran

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APA Style

Elnaz Shafigh Fard, Khalil Monfaredi, Mohammad H. Nadimi. (2014). Application Methods of Ant Colony Algorithm. American Journal of Software Engineering and Applications, 3(2), 12-20. https://doi.org/10.11648/j.ajsea.20140302.11

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ACS Style

Elnaz Shafigh Fard; Khalil Monfaredi; Mohammad H. Nadimi. Application Methods of Ant Colony Algorithm. Am. J. Softw. Eng. Appl. 2014, 3(2), 12-20. doi: 10.11648/j.ajsea.20140302.11

Copy | Download

AMA Style

Elnaz Shafigh Fard, Khalil Monfaredi, Mohammad H. Nadimi. Application Methods of Ant Colony Algorithm. Am J Softw Eng Appl. 2014;3(2):12-20. doi: 10.11648/j.ajsea.20140302.11

Copy | Download

@article{10.11648/j.ajsea.20140302.11,
  author = {Elnaz Shafigh Fard and Khalil Monfaredi and Mohammad H. Nadimi},
  title = {Application Methods of Ant Colony Algorithm},
  journal = {American Journal of Software Engineering and Applications},
  volume = {3},
  number = {2},
  pages = {12-20},
  doi = {10.11648/j.ajsea.20140302.11},
  url = {https://doi.org/10.11648/j.ajsea.20140302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20140302.11},
  abstract = {As one of the most prestigious and beneficial methods of artificial intelligence, ant colony takes the advantage of communal behavior of ants in nature for solving optimization problems in various fields. However, this useful algorithm requires extensive and repetitious computation, as a result, the processing duration of the present algorithm seems to be one of the most serious challenges about it.  In order to solve optimization problems in which duration is very important, this paper attempts to review the previously applied methods and consider the advantages and the disadvantages of each method through highlighting the problems algorithm designers encounter.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Application Methods of Ant Colony Algorithm
AU  - Elnaz Shafigh Fard
AU  - Khalil Monfaredi
AU  - Mohammad H. Nadimi
Y1  - 2014/06/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ajsea.20140302.11
DO  - 10.11648/j.ajsea.20140302.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 12
EP  - 20
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20140302.11
AB  - As one of the most prestigious and beneficial methods of artificial intelligence, ant colony takes the advantage of communal behavior of ants in nature for solving optimization problems in various fields. However, this useful algorithm requires extensive and repetitious computation, as a result, the processing duration of the present algorithm seems to be one of the most serious challenges about it.  In order to solve optimization problems in which duration is very important, this paper attempts to review the previously applied methods and consider the advantages and the disadvantages of each method through highlighting the problems algorithm designers encounter.
VL  - 3
IS  - 2
ER  -

Copy | Download