A new optimization algorithm based on ant colony system with density control strategy

Ling Qin; Yixin Chen; Ling Chen; Yuan Yao

doi:10.1007/11759966_58

A new optimization algorithm based on ant colony system with density control strategy

Ling Qin
, Yixin Chen
, Ling Chen
, Yuan Yao

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

2 Scopus citations

Abstract

A new optimization algorithm based on the ant colony system is presented by adopting the density control strategy to guarantee the performance of the algorithm. In each iteration of the algorithm, the solutions are selected to have mutation operations according to the quality and distribution of the solution. Experimental results on the traveling salesman problem show that our algorithm can not only get diversified solutions and higher convergence speed than the Neural Network Model and traditional ant colony algorithm, but also avoid the stagnation and premature problem.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings
Publisher	Springer Verlag
Pages	385-390
Number of pages	6
ISBN (Print)	354034439X, 9783540344391
DOIs	https://doi.org/10.1007/11759966_58
State	Published - 2006
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: May 28 2006 → Jun 1 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3971 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/Territory	China
City	Chengdu
Period	05/28/06 → 06/1/06

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10.1007/11759966_58

Cite this

Qin, L., Chen, Y., Chen, L., & Yao, Y. (2006). A new optimization algorithm based on ant colony system with density control strategy. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings (pp. 385-390). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS). Springer Verlag. https://doi.org/10.1007/11759966_58

Qin, Ling ; Chen, Yixin ; Chen, Ling et al. / A new optimization algorithm based on ant colony system with density control strategy. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Springer Verlag, 2006. pp. 385-390 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{19ea9b084f5049a5902fca3c10fb1720,

title = "A new optimization algorithm based on ant colony system with density control strategy",

abstract = "A new optimization algorithm based on the ant colony system is presented by adopting the density control strategy to guarantee the performance of the algorithm. In each iteration of the algorithm, the solutions are selected to have mutation operations according to the quality and distribution of the solution. Experimental results on the traveling salesman problem show that our algorithm can not only get diversified solutions and higher convergence speed than the Neural Network Model and traditional ant colony algorithm, but also avoid the stagnation and premature problem.",

author = "Ling Qin and Yixin Chen and Ling Chen and Yuan Yao",

year = "2006",

doi = "10.1007/11759966\_58",

language = "English",

isbn = "354034439X",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "385--390",

booktitle = "Advances in Neural Networks - ISNN 2006",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Qin, L, Chen, Y, Chen, L & Yao, Y 2006, A new optimization algorithm based on ant colony system with density control strategy. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3971 LNCS, Springer Verlag, pp. 385-390, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 05/28/06. https://doi.org/10.1007/11759966_58

A new optimization algorithm based on ant colony system with density control strategy. / Qin, Ling; Chen, Yixin; Chen, Ling et al.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Springer Verlag, 2006. p. 385-390 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS).

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

TY - GEN

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AU - Qin, Ling

AU - Chen, Yixin

AU - Chen, Ling

AU - Yao, Yuan

PY - 2006

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N2 - A new optimization algorithm based on the ant colony system is presented by adopting the density control strategy to guarantee the performance of the algorithm. In each iteration of the algorithm, the solutions are selected to have mutation operations according to the quality and distribution of the solution. Experimental results on the traveling salesman problem show that our algorithm can not only get diversified solutions and higher convergence speed than the Neural Network Model and traditional ant colony algorithm, but also avoid the stagnation and premature problem.

AB - A new optimization algorithm based on the ant colony system is presented by adopting the density control strategy to guarantee the performance of the algorithm. In each iteration of the algorithm, the solutions are selected to have mutation operations according to the quality and distribution of the solution. Experimental results on the traveling salesman problem show that our algorithm can not only get diversified solutions and higher convergence speed than the Neural Network Model and traditional ant colony algorithm, but also avoid the stagnation and premature problem.

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U2 - 10.1007/11759966_58

DO - 10.1007/11759966_58

M3 - Conference contribution

AN - SCOPUS:33745909398

SN - 354034439X

SN - 9783540344391

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 385

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BT - Advances in Neural Networks - ISNN 2006

PB - Springer Verlag

T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

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Qin L, Chen Y, Chen L, Yao Y. A new optimization algorithm based on ant colony system with density control strategy. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Springer Verlag. 2006. p. 385-390. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11759966_58

A new optimization algorithm based on ant colony system with density control strategy

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