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Ant Colony Optimization with Genetic Operations

Received: 12 June 2013     Published: 30 June 2013
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Abstract

This paper attempts to overcome stagnation problem of Ant Colony Optimization (ACO) algorithms. Stagnation is undesirable state which occurs at a later phases of the search process. Excessive pheromone values attract more ants and make further exploration hardly possible. This problem has been addressed by Genetic operations (GO) incorporated into ACO framework. Crossover and mutation operations have been adapted for use with ant generated strings which still have to provide feasible solutions. Genetic operations decrease selection pressure and increase probability of finding the global optimum. Extensive simulation tests were made in order to determine influence of genetic operation on algorithm performance.

Published in Automation, Control and Intelligent Systems (Volume 1, Issue 3)
DOI 10.11648/j.acis.20130103.13
Page(s) 53-58
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), 2013. Published by Science Publishing Group

Keywords

Ant Colony Optimization, Genetic Operations, Crossover, Mutation, Minimal Path Search

References
[1] P. E. Hart, N. J. Nilsson and B. Raphael, A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics SSC4 4(2), 1968, 100–107
[2] M. Dorigo, G. Caro and L. Gambardella, Ant algorithms for discrete optimization, Artificial Life, 5(2), 1999, 137-172
[3] L. Gambardella and M. Dorigo, Solving symmetric and asymmetric TSPs by ant colonies, In Proceedings of the IEEE Conference on Evolutionary Computation, ICEC96, IEEE Press, 1996, 622–627
[4] Y. Nakamichi and T. Arita, Diversity control in ant colony optimization, In Abbas HA (ed) Proceedings of the Inaugural Workshop on Artificial Life (AL'01), Adelaide, Australia, Dec 11, 2001, 70-78
[5] R. Kumar M. K. Tiwari and R. Shankar, Scheduling of flexible manufacturing systems: An ant colony optimization approach, proc. Instn. Mech. Engrs Vol. 217 Part B: J. Engineering Manufacture, 2003, 1443–1453
[6] J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, 1975
[7] I Sekaj, Evolucne vypocty a ich vyuzitie v praxi, IRIS Press, 2005
[8] M. Becker and H. Szczerbicka, Parameters influencing the performance of ant algorithms applied to optimization of buffer size in manufacturing, IEMS Vol. 4, No. 2, December 2005, 184–191
[9] M. Ciba, ACO algorithm with macro cycles, Proceedings on 14th Conference of Doctorial Students on Elitech’12, Slovak Technical University of Bratislava, May 2012
Cite This Article
  • APA Style

    Matej Ciba, Ivan Sekaj. (2013). Ant Colony Optimization with Genetic Operations. Automation, Control and Intelligent Systems, 1(3), 53-58. https://doi.org/10.11648/j.acis.20130103.13

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    ACS Style

    Matej Ciba; Ivan Sekaj. Ant Colony Optimization with Genetic Operations. Autom. Control Intell. Syst. 2013, 1(3), 53-58. doi: 10.11648/j.acis.20130103.13

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    AMA Style

    Matej Ciba, Ivan Sekaj. Ant Colony Optimization with Genetic Operations. Autom Control Intell Syst. 2013;1(3):53-58. doi: 10.11648/j.acis.20130103.13

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  • @article{10.11648/j.acis.20130103.13,
      author = {Matej Ciba and Ivan Sekaj},
      title = {Ant Colony Optimization with Genetic Operations},
      journal = {Automation, Control and Intelligent Systems},
      volume = {1},
      number = {3},
      pages = {53-58},
      doi = {10.11648/j.acis.20130103.13},
      url = {https://doi.org/10.11648/j.acis.20130103.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130103.13},
      abstract = {This paper attempts to overcome stagnation problem of Ant Colony Optimization (ACO) algorithms. Stagnation is undesirable state which occurs at a later phases of the search process. Excessive pheromone values attract more ants and make further exploration hardly possible. This problem has been addressed by Genetic operations (GO) incorporated into ACO framework. Crossover and mutation operations have been adapted for use with ant generated strings which still have to provide feasible solutions. Genetic operations decrease selection pressure and increase probability of finding the global optimum. Extensive simulation tests were made in order to determine influence of genetic operation on algorithm performance.},
     year = {2013}
    }
    

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  • TY  - JOUR
    T1  - Ant Colony Optimization with Genetic Operations
    AU  - Matej Ciba
    AU  - Ivan Sekaj
    Y1  - 2013/06/30
    PY  - 2013
    N1  - https://doi.org/10.11648/j.acis.20130103.13
    DO  - 10.11648/j.acis.20130103.13
    T2  - Automation, Control and Intelligent Systems
    JF  - Automation, Control and Intelligent Systems
    JO  - Automation, Control and Intelligent Systems
    SP  - 53
    EP  - 58
    PB  - Science Publishing Group
    SN  - 2328-5591
    UR  - https://doi.org/10.11648/j.acis.20130103.13
    AB  - This paper attempts to overcome stagnation problem of Ant Colony Optimization (ACO) algorithms. Stagnation is undesirable state which occurs at a later phases of the search process. Excessive pheromone values attract more ants and make further exploration hardly possible. This problem has been addressed by Genetic operations (GO) incorporated into ACO framework. Crossover and mutation operations have been adapted for use with ant generated strings which still have to provide feasible solutions. Genetic operations decrease selection pressure and increase probability of finding the global optimum. Extensive simulation tests were made in order to determine influence of genetic operation on algorithm performance.
    VL  - 1
    IS  - 3
    ER  - 

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Author Information
  • Institute of Control and Industrial Informatics, Bratislava, Slovakia

  • Institute of Control and Industrial Informatics, Bratislava, Slovakia

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