Route planning for agent-based information retrieval

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dc.contributor.author Sygkouna, I en
dc.contributor.author Drakos, M-P en
dc.contributor.author Anagnostou, M en
dc.date.accessioned 2014-03-01T01:34:27Z
dc.date.available 2014-03-01T01:34:27Z
dc.date.issued 2010 en
dc.identifier.issn 0926-6003 en
dc.identifier.uri http://hdl.handle.net/123456789/20742
dc.subject Distributed information retrieval en
dc.subject Mobile agents en
dc.subject NP-hard problems en
dc.subject Planning en
dc.subject.classification Operations Research & Management Science en
dc.subject.classification Mathematics, Applied en
dc.subject.other Agent based en
dc.subject.other Distributed data en
dc.subject.other Distributed information retrieval en
dc.subject.other Near-optimal solutions en
dc.subject.other Network node en
dc.subject.other NP-HARD problem en
dc.subject.other Polynomial-time algorithms en
dc.subject.other Route planning en
dc.subject.other Simulation result en
dc.subject.other Speed-ups en
dc.subject.other Task completion time en
dc.subject.other Computational complexity en
dc.subject.other Information retrieval en
dc.subject.other Multi agent systems en
dc.subject.other Polynomials en
dc.subject.other Mobile agents en
dc.title Route planning for agent-based information retrieval en
heal.type journalArticle en
heal.identifier.primary 10.1007/s10589-008-9204-7 en
heal.identifier.secondary http://dx.doi.org/10.1007/s10589-008-9204-7 en
heal.language English en
heal.publicationDate 2010 en
heal.abstract This article focuses on Mobile Agents and their use for information retrieval. A multi-agent system is considered; a number of agents are involved in a collective effort to retrieve distributed data from network nodes. Increasing the number of agents may speed-up information retrieval but is burdensome to performance. Initiating with a given number of agents, our objective is to determine the routes of the agents so that the task completion time is minimized. Two known and one new polynomial-time algorithms are tested that produce near-optimal solutions. Simulation results show the cases for which each one is most effective. Additionally, we study the influence of various parameters on the solution. By parametrically varying the number of agents, our method can be used to determine the minimum number that satisfies the desired trade-off between time and performance. © 2008 Springer Science+Business Media, LLC. en
heal.publisher SPRINGER en
heal.journalName Computational Optimization and Applications en
dc.identifier.doi 10.1007/s10589-008-9204-7 en
dc.identifier.isi ISI:000282060800004 en
dc.identifier.volume 47 en
dc.identifier.issue 1 en
dc.identifier.spage 77 en
dc.identifier.epage 96 en

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