Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30828
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mavrovouniotis, Michalis | - |
dc.contributor.author | Yang, Shengxiang | - |
dc.contributor.author | Van, Mien | - |
dc.contributor.author | Li, Changhe | - |
dc.contributor.author | Polycarpou, Marios M. | - |
dc.date.accessioned | 2023-11-20T12:35:49Z | - |
dc.date.available | 2023-11-20T12:35:49Z | - |
dc.date.issued | 2020-02-01 | - |
dc.identifier.citation | IEEE Computational Intelligence Magazine, 2020, vol. 15, iss. 1, pp. 52 - 63 | en_US |
dc.identifier.issn | 1556603X | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/30828 | - |
dc.description.abstract | Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE Computational Intelligence Magazine | en_US |
dc.rights | © IEEE | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Combinatorial optimization | en_US |
dc.subject | Ant Colony Optimization algorithms | en_US |
dc.subject | Combinatorial optimization problems | en_US |
dc.title | Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem [Research Frontier] | en_US |
dc.type | Article | en_US |
dc.collaboration | University of Cyprus | en_US |
dc.collaboration | De Montfort University | en_US |
dc.collaboration | Queen’s University Belfast | en_US |
dc.collaboration | China University of Geosciences | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | Cyprus | en_US |
dc.country | United Kingdom | en_US |
dc.country | China | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1109/MCI.2019.2954644 | en_US |
dc.identifier.scopus | 2-s2.0-85078224327 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85078224327 | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
dc.relation.issue | 1 | en_US |
dc.relation.volume | 15 | en_US |
cut.common.academicyear | 2019-2020 | en_US |
dc.identifier.spage | 52 | en_US |
dc.identifier.epage | 63 | en_US |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
crisitem.author.orcid | 0000-0002-5281-4175 | - |
Appears in Collections: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
20
37
checked on Mar 14, 2024
Page view(s) 20
98
Last Week
1
1
Last month
3
3
checked on Oct 5, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.