American Journal of Software Engineering and Applications

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Application Methods of Ant Colony Algorithm

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.

DOI 10.11648/j.ajsea.20140302.11
Published in American Journal of Software Engineering and Applications (Volume 3, Issue 2, April 2014)
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), 2024. Published by Science Publishing Group

Keywords

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

References
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[13] M. Pedemonte, H. Cancela, A cellular ant colony optimisation for the generalized Steiner problem, International Journal of Innovative Computing and Applications 2 (3) (2010) 188–201.
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[15] P. Delisle, M. Gravel, M. Krajecki, C. Gagné, W. Price, Comparing parallelization of an ACO: message passing vs. shared memory, in: Proceedings of the 2nd International Workshop on Hybrid Metaheuristics, Lecture Notes in Computer Science vol. 3636 (2005) 1–11
[16] R. Michel, M. Middendorf, An island model based ant system with lookahead for the shortest supersequence problem, in: Proceedings of the 5th International.
[17] R.Vaidyanathan and J.L.Trahan :Dynamic Reconfiguration :Architectures and Algorithms .Kluwer,(2004 )
[18] S.Li,M.Hao Yang ,Chung-Wei WENG,Yi –Hong Chen Ant Colony Optimization design and its FPGA implementation
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Author Information
  • Faculty of Computer Engineering, Najafabad branch, Islamic Azad University, Isfahan, Iran

  • Engineering Faculty, Department of Electrical and Electronic Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

  • 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

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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://download.sciencepg.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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    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
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    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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