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 |
Ant Colony Optimization, Genetic Operations, Crossover, Mutation, Minimal Path Search
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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
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
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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Download@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}
}
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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