A diversity guaranteed ant colony algorithm based on immune strategy

Qin Ling; Chen Yixin; Luo Jianli; Chen Ling; Guo Jing

doi:10.1109/IMSCCS.2006.252

A diversity guaranteed ant colony algorithm based on immune strategy

Qin Ling
, Chen Yixin
, Luo Jianli
, Chen Ling
, Guo Jing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

A diversity guaranteed ant colony algorithm is presented by simulating the behavior of biological immune system, The algorithm adopts the immunogenic methods immune selection, immune memory, immune metabolism, density control and isolation niche technique. In each iteration of the algorithm, the solutions of the ants are selected to have crossover and mutation operations according to their quality and the distribution of the solutions. The mutation probability is determined by the diversity of the solutions. Experimental results on the traveling salesman problem show that our algorithm can obtain high quality of solutions, high convergence speed. It can avoid the stagnation and premature phenomena and has strong capability of optimization.

Original language	English
Title of host publication	First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06
Pages	217-223
Number of pages	7
DOIs	https://doi.org/10.1109/IMSCCS.2006.252
State	Published - 2006
Event	First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06 - Hangzhou, Zhejiang, China Duration: Apr 20 2006 → Apr 24 2006

Publication series

Name	First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06
Volume	2

Conference

Conference	First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06
Country/Territory	China
City	Hangzhou, Zhejiang
Period	04/20/06 → 04/24/06

Access to Document

10.1109/IMSCCS.2006.252

Cite this

Ling, Q., Yixin, C., Jianli, L., Ling, C., & Jing, G. (2006). A diversity guaranteed ant colony algorithm based on immune strategy. In First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06 (pp. 217-223). Article 1633889 (First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06; Vol. 2). https://doi.org/10.1109/IMSCCS.2006.252

@inproceedings{17f654abb5fe40f6a8275baa923cb51e,

title = "A diversity guaranteed ant colony algorithm based on immune strategy",

abstract = "A diversity guaranteed ant colony algorithm is presented by simulating the behavior of biological immune system, The algorithm adopts the immunogenic methods immune selection, immune memory, immune metabolism, density control and isolation niche technique. In each iteration of the algorithm, the solutions of the ants are selected to have crossover and mutation operations according to their quality and the distribution of the solutions. The mutation probability is determined by the diversity of the solutions. Experimental results on the traveling salesman problem show that our algorithm can obtain high quality of solutions, high convergence speed. It can avoid the stagnation and premature phenomena and has strong capability of optimization.",

author = "Qin Ling and Chen Yixin and Luo Jianli and Chen Ling and Guo Jing",

year = "2006",

doi = "10.1109/IMSCCS.2006.252",

language = "English",

isbn = "0769525814",

series = "First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06",

pages = "217--223",

booktitle = "First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06",

note = "First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06 ; Conference date: 20-04-2006 Through 24-04-2006",

}

Ling, Q, Yixin, C, Jianli, L, Ling, C & Jing, G 2006, A diversity guaranteed ant colony algorithm based on immune strategy. in First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06., 1633889, First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06, vol. 2, pp. 217-223, First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06, Hangzhou, Zhejiang, China, 04/20/06. https://doi.org/10.1109/IMSCCS.2006.252

A diversity guaranteed ant colony algorithm based on immune strategy. / Ling, Qin; Yixin, Chen; Jianli, Luo et al.
First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06. 2006. p. 217-223 1633889 (First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A diversity guaranteed ant colony algorithm based on immune strategy

AU - Ling, Qin

AU - Yixin, Chen

AU - Jianli, Luo

AU - Ling, Chen

AU - Jing, Guo

PY - 2006

Y1 - 2006

N2 - A diversity guaranteed ant colony algorithm is presented by simulating the behavior of biological immune system, The algorithm adopts the immunogenic methods immune selection, immune memory, immune metabolism, density control and isolation niche technique. In each iteration of the algorithm, the solutions of the ants are selected to have crossover and mutation operations according to their quality and the distribution of the solutions. The mutation probability is determined by the diversity of the solutions. Experimental results on the traveling salesman problem show that our algorithm can obtain high quality of solutions, high convergence speed. It can avoid the stagnation and premature phenomena and has strong capability of optimization.

AB - A diversity guaranteed ant colony algorithm is presented by simulating the behavior of biological immune system, The algorithm adopts the immunogenic methods immune selection, immune memory, immune metabolism, density control and isolation niche technique. In each iteration of the algorithm, the solutions of the ants are selected to have crossover and mutation operations according to their quality and the distribution of the solutions. The mutation probability is determined by the diversity of the solutions. Experimental results on the traveling salesman problem show that our algorithm can obtain high quality of solutions, high convergence speed. It can avoid the stagnation and premature phenomena and has strong capability of optimization.

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DO - 10.1109/IMSCCS.2006.252

M3 - Conference contribution

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SN - 0769525814

SN - 9780769525815

T3 - First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06

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EP - 223

BT - First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06

T2 - First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06

Y2 - 20 April 2006 through 24 April 2006

ER -

A diversity guaranteed ant colony algorithm based on immune strategy

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