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A new minimum pheromone threshold strategy (MPTS) for max-min ant system

In recent years, various metaheuristic approaches have been created to solve quadratic assignment problems (QAPs). Among others is the ant colony optimization (ACO) algorithm, which was inspired by the foraging behavior of ants. Although it has solved some QAPs successfully, it still contains some w...

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Main Authors: Wong, K. Y., See, P. C.
Format: Article
Published: Elsevier Science 2009
Subjects:
HD28 Management. Industrial Management
Online Access:http://eprints.utm.my/id/eprint/11798/
http://dx.doi.org/10.1016/j.asoc.2008.11.011
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spelling my.utm.117982011-01-18T07:26:42Z http://eprints.utm.my/id/eprint/11798/ A new minimum pheromone threshold strategy (MPTS) for max-min ant system Wong, K. Y. See, P. C. HD28 Management. Industrial Management In recent years, various metaheuristic approaches have been created to solve quadratic assignment problems (QAPs). Among others is the ant colony optimization (ACO) algorithm, which was inspired by the foraging behavior of ants. Although it has solved some QAPs successfully, it still contains some weaknesses and is unable to solve large QAP instances effectively. Thereafter, various suggestions have been made to improve the performance of the ACO algorithm. One of them is through the development of the max–min ant system (MMAS) algorithm. In this paper, a discussion will be given on the working structure of MMAS and its associated weaknesses or limitations. A new strategy that could further improve the search performance of MMAS will then be presented. Finally, the results of an experimental evaluation conducted to evaluate the usefulness of this new strategy will be described. Elsevier Science 2009-06 Article PeerReviewed Wong, K. Y. and See, P. C. (2009) A new minimum pheromone threshold strategy (MPTS) for max-min ant system. Applied Soft Computing Journal, 9 (3). pp. 882-888. ISSN 1568-4946 http://dx.doi.org/10.1016/j.asoc.2008.11.011 doi:10.1016/j.asoc.2008.11.011
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic HD28 Management. Industrial Management
spellingShingle HD28 Management. Industrial Management
Wong, K. Y.
See, P. C.
A new minimum pheromone threshold strategy (MPTS) for max-min ant system
description In recent years, various metaheuristic approaches have been created to solve quadratic assignment problems (QAPs). Among others is the ant colony optimization (ACO) algorithm, which was inspired by the foraging behavior of ants. Although it has solved some QAPs successfully, it still contains some weaknesses and is unable to solve large QAP instances effectively. Thereafter, various suggestions have been made to improve the performance of the ACO algorithm. One of them is through the development of the max–min ant system (MMAS) algorithm. In this paper, a discussion will be given on the working structure of MMAS and its associated weaknesses or limitations. A new strategy that could further improve the search performance of MMAS will then be presented. Finally, the results of an experimental evaluation conducted to evaluate the usefulness of this new strategy will be described.
format Article
author Wong, K. Y.
See, P. C.
author_facet Wong, K. Y.
See, P. C.
author_sort Wong, K. Y.
title A new minimum pheromone threshold strategy (MPTS) for max-min ant system
title_short A new minimum pheromone threshold strategy (MPTS) for max-min ant system
title_full A new minimum pheromone threshold strategy (MPTS) for max-min ant system
title_fullStr A new minimum pheromone threshold strategy (MPTS) for max-min ant system
title_full_unstemmed A new minimum pheromone threshold strategy (MPTS) for max-min ant system
title_sort new minimum pheromone threshold strategy (mpts) for max-min ant system
publisher Elsevier Science
publishDate 2009
url http://eprints.utm.my/id/eprint/11798/
http://dx.doi.org/10.1016/j.asoc.2008.11.011
_version_ 1643645778139283456
score 13.211869

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