An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy
Summary :
The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.11 pp.2309-2318
- Publication Date
- 2013/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.2309
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Pooia LALBAKHSH
Islamic Azad University-Borujerd Branch
Bahram ZAERI
Islamic Azad University-Arak Branch
Ali LALBAKHSH
Islamic Azad University, Kermanshah Branch
Keyword
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Pooia LALBAKHSH, Bahram ZAERI, Ali LALBAKHSH, "An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 11, pp. 2309-2318, November 2013, doi: 10.1587/transinf.E96.D.2309.
Abstract: The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2309/_p
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@ARTICLE{e96-d_11_2309,
author={Pooia LALBAKHSH, Bahram ZAERI, Ali LALBAKHSH, },
journal={IEICE TRANSACTIONS on Information},
title={An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy},
year={2013},
volume={E96-D},
number={11},
pages={2309-2318},
abstract={The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.},
keywords={},
doi={10.1587/transinf.E96.D.2309},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy
T2 - IEICE TRANSACTIONS on Information
SP - 2309
EP - 2318
AU - Pooia LALBAKHSH
AU - Bahram ZAERI
AU - Ali LALBAKHSH
PY - 2013
DO - 10.1587/transinf.E96.D.2309
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2013
AB - The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.
ER -
English
