IEICE TRANSACTIONS on Information
Improved Clonal Selection Algorithm Combined with Ant Colony Optimization
Summary :
Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.6 pp.1813-1823
- Publication Date
- 2008/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.6.1813
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
Authors
Keyword
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Shangce GAO, Wei WANG, Hongwei DAI, Fangjia LI, Zheng TANG, "Improved Clonal Selection Algorithm Combined with Ant Colony Optimization" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 6, pp. 1813-1823, June 2008, doi: 10.1093/ietisy/e91-d.6.1813.
Abstract: Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.6.1813/_p
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@ARTICLE{e91-d_6_1813,
author={Shangce GAO, Wei WANG, Hongwei DAI, Fangjia LI, Zheng TANG, },
journal={IEICE TRANSACTIONS on Information},
title={Improved Clonal Selection Algorithm Combined with Ant Colony Optimization},
year={2008},
volume={E91-D},
number={6},
pages={1813-1823},
abstract={Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.},
keywords={},
doi={10.1093/ietisy/e91-d.6.1813},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Improved Clonal Selection Algorithm Combined with Ant Colony Optimization
T2 - IEICE TRANSACTIONS on Information
SP - 1813
EP - 1823
AU - Shangce GAO
AU - Wei WANG
AU - Hongwei DAI
AU - Fangjia LI
AU - Zheng TANG
PY - 2008
DO - 10.1093/ietisy/e91-d.6.1813
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2008
AB - Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.
ER -
English
