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- Publisher Website: 10.1109/CEC.2004.1330938
- Scopus: eid_2-s2.0-4344689783
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Conference Paper: Autonomous agent response learning by a multi-species particle swarm optimization
| Title | Autonomous agent response learning by a multi-species particle swarm optimization |
|---|---|
| Authors | |
| Issue Date | 2004 |
| Publisher | IEEE. 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? |
| Abstract | A 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 Identifier | http://hdl.handle.net/10722/196694 |
| ISBN |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chow, CK | - |
| dc.contributor.author | Tsui, HT | - |
| dc.date.accessioned | 2014-04-24T02:10:34Z | - |
| dc.date.available | 2014-04-24T02:10:34Z | - |
| dc.date.issued | 2004 | - |
| dc.identifier.citation | The 2004 Congress on Evolutionary Computation (CEC 2004), Portland, OR., 19-23 June 2004. In Conference Proceedings, 2004, v. 1, p. 778-785 | - |
| dc.identifier.isbn | 978-078038515-3 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/196694 | - |
| dc.description.abstract | A 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.language | eng | - |
| dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9256 | - |
| dc.relation.ispartof | Congress on Evolutionary Computation, CEC 2004 Proceedings | - |
| dc.rights | ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
| dc.title | Autonomous agent response learning by a multi-species particle swarm optimization | - |
| dc.type | Conference_Paper | - |
| dc.description.nature | published_or_final_version | - |
| dc.identifier.doi | 10.1109/CEC.2004.1330938 | - |
| dc.identifier.scopus | eid_2-s2.0-4344689783 | - |
| dc.identifier.volume | 1 | - |
| dc.identifier.spage | 778 | - |
| dc.identifier.epage | 785 | - |
| dc.publisher.place | United States | - |
| dc.customcontrol.immutable | sml 160603 amended | - |