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dc.contributor.authorYang, Shengxiangen
dc.contributor.authorCheng, Huien
dc.date.accessioned2013-05-16T09:13:47Z
dc.date.available2013-05-16T09:13:47Z
dc.date.issued2010
dc.identifier.citationCheng, H. and Yang, S. (2010) Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Engineering Applications of Artificial Intelligence, 23(5), August 2010, pp. 806-819.en
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/2086/8524
dc.description.abstractIn this paper, the problem of dynamic quality-of-service (QoS) multicast routing in mobile ad hoc networks is investigated. Lots of interesting works have been done on multicast since it is proved to be a NP-hard problem. However, most of them consider the static network scenarios only and the multicast tree cannot adapt to the topological changes. With the advancement in communication technologies, more and more wireless mobile networks appear, e.g., mobile ad hoc networks (MANETs). In a MANET, the network topology keeps changing due to its inherent characteristics such as the node mobility and energy conservation. Therefore, an effective multicast algorithm should track the topological changes and adapt the best multicast tree to the changes accordingly. In this paper, we propose to use genetic algorithms with immigrants schemes to solve the dynamic QoS multicast problem in MANETs. MANETs are considered as target systems because they represent a new generation of wireless networks. In the construction of the dynamic network environments, two models are proposed and investigated. One is named as the general dynamics model in which the topologies are changed due to that the nodes are scheduled to sleep or wake up. The other is named as the worst dynamics model, in which the topologies are altered because some links on the current best multicast tree are removed. Extensive experiments are conducted based on both of the dynamic network models. The experimental results show that these immigrants based genetic algorithms can quickly adapt to the environmental changes (i.e., the network topology changes) and produce high quality solutions following each change.en
dc.language.isoenen
dc.publisherIFAC, the International Federation of Automatic Controlen
dc.subjectMobile ad hoc networken
dc.subjectDynamic multicasten
dc.subjectGenetic algorithmen
dc.subjectImmigrants schemeen
dc.subjectDynamic optimizationen
dc.titleGenetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.engappai.2010.01.021
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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