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Conference Paper: Autonomous agent response learning by a multi-species particle swarm optimization

TitleAutonomous agent response learning by a multi-species particle swarm optimization
Authors
Issue Date2004
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9256
Citation
The 2004 Congress on Evolutionary Computation (CEC 2004), Portland, OR., 19-23 June 2004. In Conference Proceedings, 2004, v. 1, p. 778-785 How to Cite?
AbstractA novel autonomous agent response learning (AARL) algorithm is presented in this paper. We proposed to decompose the award function into a set of local award functions. By optimizing this objective function set, the response function with maximum award can be determined. To tackle the optimization problem, a modified Particle Swarm Optimization (PSO) called "Multi-Species PSO (MS-PSO)" is introduced by considering each objective function as a specie swarm. Two sets of experiments are provided to illustrate the performance of MS-PSO. The results show that it returns a more accurate response set within shorter duration by comparing with other PSO methods.
Persistent Identifierhttp://hdl.handle.net/10722/196694
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChow, CK-
dc.contributor.authorTsui, HT-
dc.date.accessioned2014-04-24T02:10:34Z-
dc.date.available2014-04-24T02:10:34Z-
dc.date.issued2004-
dc.identifier.citationThe 2004 Congress on Evolutionary Computation (CEC 2004), Portland, OR., 19-23 June 2004. In Conference Proceedings, 2004, v. 1, p. 778-785-
dc.identifier.isbn978-078038515-3-
dc.identifier.urihttp://hdl.handle.net/10722/196694-
dc.description.abstractA novel autonomous agent response learning (AARL) algorithm is presented in this paper. We proposed to decompose the award function into a set of local award functions. By optimizing this objective function set, the response function with maximum award can be determined. To tackle the optimization problem, a modified Particle Swarm Optimization (PSO) called "Multi-Species PSO (MS-PSO)" is introduced by considering each objective function as a specie swarm. Two sets of experiments are provided to illustrate the performance of MS-PSO. The results show that it returns a more accurate response set within shorter duration by comparing with other PSO methods.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9256-
dc.relation.ispartofCongress on Evolutionary Computation, CEC 2004 Proceedings-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsCongress on Evolutionary Computation, CEC 2004 Proceedings. Copyright © IEEE.-
dc.titleAutonomous agent response learning by a multi-species particle swarm optimization-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2004.1330938-
dc.identifier.scopuseid_2-s2.0-4344689783-
dc.identifier.volume1-
dc.identifier.spage778-
dc.identifier.epage785-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160603 amended-

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