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

Computer Science

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

17 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.

Original language	English
Title of host publication	2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	9-16
Number of pages	8
ISBN (Print)	9781479945160
DOIs	https://doi.org/10.1109/CIDUE.2014.7007861
Publication status	Published - 12 Jan 2015
Event	2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014

Conference

Conference	2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Science Applications

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10.1109/CIDUE.2014.7007861

Cite this

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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.",

author = "Michalis Mavrovouniotis and Shengxiang Yang and Xin Yao",

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doi = "10.1109/CIDUE.2014.7007861",

language = "English",

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booktitle = "2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014 ; Conference date: 09-12-2014 Through 12-12-2014",

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Mavrovouniotis, M, Yang, S & Yao, X 2015, Multi-colony ant algorithms for the dynamic travelling salesman problem. in 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Proceedings., 7007861, Institute of Electrical and Electronics Engineers (IEEE), pp. 9-16, 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014, Orlando, United States, 9/12/14. https://doi.org/10.1109/CIDUE.2014.7007861

Multi-colony ant algorithms for the dynamic travelling salesman problem. / Mavrovouniotis, Michalis; Yang, Shengxiang; Yao, Xin.
2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 9-16 7007861.

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

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PY - 2015/1/12

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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.

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.

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Y2 - 9 December 2014 through 12 December 2014

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Multi-colony ant algorithms for the dynamic travelling salesman problem

Abstract

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ASJC Scopus subject areas

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