Route planning for agent-based information retrieval

Sygkouna, I; Drakos, M-P; Anagnostou, M

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	https://dspace.lib.ntua.gr/xmlui/handle/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