Multi-colony ant algorithms for the dynamic travelling salesman problem

Michalis MAVROVOUNIOTIS; Shengxiang YANG; Xin YAO

doi:10.1109/CIDUE.2014.7007861

Multi-colony ant algorithms for the dynamic travelling salesman problem

Michalis MAVROVOUNIOTIS
, Shengxiang YANG
, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

30 Citations (Scopus)

Abstract

A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms. © 2014 IEEE.

Original language	English
Title of host publication	IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	9-16
Number of pages	8
ISBN (Print)	9781479945160
DOIs	https://doi.org/10.1109/CIDUE.2014.7007861
Publication status	Published - Dec 2014
Externally published	Yes

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MAVROVOUNIOTIS, M., YANG, S., & YAO, X. (2014). Multi-colony ant algorithms for the dynamic travelling salesman problem. In IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings (pp. 9-16). Article 7007861 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIDUE.2014.7007861

MAVROVOUNIOTIS, Michalis ; YANG, Shengxiang ; YAO, Xin. / Multi-colony ant algorithms for the dynamic travelling salesman problem. IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 9-16

@inproceedings{7eb71ce87fca423baf3c945cba8e6f5e,

title = "Multi-colony ant algorithms for the dynamic travelling salesman problem",

abstract = "A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms. {\textcopyright} 2014 IEEE.",

author = "Michalis MAVROVOUNIOTIS and Shengxiang YANG and Xin YAO",

year = "2014",

month = dec,

doi = "10.1109/CIDUE.2014.7007861",

language = "English",

isbn = "9781479945160",

pages = "9--16",

booktitle = "IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

MAVROVOUNIOTIS, M, YANG, S & YAO, X 2014, Multi-colony ant algorithms for the dynamic travelling salesman problem. in IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings., 7007861, Institute of Electrical and Electronics Engineers Inc., pp. 9-16. https://doi.org/10.1109/CIDUE.2014.7007861

Multi-colony ant algorithms for the dynamic travelling salesman problem. / MAVROVOUNIOTIS, Michalis; YANG, Shengxiang; YAO, Xin.
IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 9-16 7007861.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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T1 - Multi-colony ant algorithms for the dynamic travelling salesman problem

AU - MAVROVOUNIOTIS, Michalis

AU - YANG, Shengxiang

AU - YAO, Xin

PY - 2014/12

Y1 - 2014/12

N2 - A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms. © 2014 IEEE.

AB - A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms. © 2014 IEEE.

UR - https://www.scopus.com/pages/publications/84922928085

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DO - 10.1109/CIDUE.2014.7007861

M3 - Conference paper (refereed)

SN - 9781479945160

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BT - IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

MAVROVOUNIOTIS M, YANG S, YAO X. Multi-colony ant algorithms for the dynamic travelling salesman problem. In IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 9-16. 7007861 doi: 10.1109/CIDUE.2014.7007861

Multi-colony ant algorithms for the dynamic travelling salesman problem

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