Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30858
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mavrovouniotis, Michalis | - |
dc.contributor.author | Yang, Shengxiang | - |
dc.contributor.author | Yao, Xin | - |
dc.date.accessioned | 2023-11-27T10:45:31Z | - |
dc.date.available | 2023-11-27T10:45:31Z | - |
dc.date.issued | 2014-01-12 | - |
dc.identifier.citation | 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014, Orlando, Florida, 9 - 12 December 2014 | en_US |
dc.identifier.isbn | 9781479945160 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/30858 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.rights | © IEEE | en_US |
dc.subject | Ant colony optimization | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | ACO algorithms | en_US |
dc.subject | Ant Colony Optimization algorithms | en_US |
dc.subject | Dynamic environments | en_US |
dc.subject | Multi-colony ant algorithms | en_US |
dc.subject | Optimization problems | en_US |
dc.subject | Search area | en_US |
dc.subject | Stationary environments | en_US |
dc.subject | Travelling salesman problem | en_US |
dc.subject | Traveling salesman problem | en_US |
dc.title | Multi-colony ant algorithms for the dynamic travelling salesman problem | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | De Montfort University | en_US |
dc.collaboration | University of Birmingham | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.country | United Kingdom | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.relation.conference | IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings | en_US |
dc.identifier.doi | 10.1109/CIDUE.2014.7007861 | en_US |
dc.identifier.scopus | 2-s2.0-84922928085 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/84922928085 | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
cut.common.academicyear | 2014-2015 | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
crisitem.author.orcid | 0000-0002-5281-4175 | - |
Appears in Collections: | Άρθρα/Articles |
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