IEICE TRANSACTIONS on Fundamentals
An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems
Summary :
The clonal selection algorithm (CS), inspired by the basic features of adaptive immune response to antigenic stimulus, can exploit and explore the solution space parallelly and effectively. However, antibody initialization and premature convergence are two problems of CS. To overcome these two problems, we propose a chaotic distance-based clonal selection algorithm (CDCS). In this novel algorithm, we introduce a chaotic initialization mechanism and a distance-based somatic hypermutation to improve the performance of CS. The proposed algorithm is also verified for numerous benchmark traveling salesman problems. Experimental results show that the improved algorithm proposed in this paper provides better performance when compared to other metaheuristics.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.12 pp.2930-2938
- Publication Date
- 2007/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e90-a.12.2930
- Type of Manuscript
- PAPER
- Category
- Neural Networks and Bioengineering
Authors
Keyword
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Shangce GAO, Zheng TANG, Hongwei DAI, Jianchen ZHANG, "An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 12, pp. 2930-2938, December 2007, doi: 10.1093/ietfec/e90-a.12.2930.
Abstract: The clonal selection algorithm (CS), inspired by the basic features of adaptive immune response to antigenic stimulus, can exploit and explore the solution space parallelly and effectively. However, antibody initialization and premature convergence are two problems of CS. To overcome these two problems, we propose a chaotic distance-based clonal selection algorithm (CDCS). In this novel algorithm, we introduce a chaotic initialization mechanism and a distance-based somatic hypermutation to improve the performance of CS. The proposed algorithm is also verified for numerous benchmark traveling salesman problems. Experimental results show that the improved algorithm proposed in this paper provides better performance when compared to other metaheuristics.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.12.2930/_p
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@ARTICLE{e90-a_12_2930,
author={Shangce GAO, Zheng TANG, Hongwei DAI, Jianchen ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems},
year={2007},
volume={E90-A},
number={12},
pages={2930-2938},
abstract={The clonal selection algorithm (CS), inspired by the basic features of adaptive immune response to antigenic stimulus, can exploit and explore the solution space parallelly and effectively. However, antibody initialization and premature convergence are two problems of CS. To overcome these two problems, we propose a chaotic distance-based clonal selection algorithm (CDCS). In this novel algorithm, we introduce a chaotic initialization mechanism and a distance-based somatic hypermutation to improve the performance of CS. The proposed algorithm is also verified for numerous benchmark traveling salesman problems. Experimental results show that the improved algorithm proposed in this paper provides better performance when compared to other metaheuristics.},
keywords={},
doi={10.1093/ietfec/e90-a.12.2930},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2930
EP - 2938
AU - Shangce GAO
AU - Zheng TANG
AU - Hongwei DAI
AU - Jianchen ZHANG
PY - 2007
DO - 10.1093/ietfec/e90-a.12.2930
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2007
AB - The clonal selection algorithm (CS), inspired by the basic features of adaptive immune response to antigenic stimulus, can exploit and explore the solution space parallelly and effectively. However, antibody initialization and premature convergence are two problems of CS. To overcome these two problems, we propose a chaotic distance-based clonal selection algorithm (CDCS). In this novel algorithm, we introduce a chaotic initialization mechanism and a distance-based somatic hypermutation to improve the performance of CS. The proposed algorithm is also verified for numerous benchmark traveling salesman problems. Experimental results show that the improved algorithm proposed in this paper provides better performance when compared to other metaheuristics.
ER -
English
