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Article: Particle swarm optimization with a leader and followers
Title | Particle swarm optimization with a leader and followers |
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Authors | |
Keywords | Particle swarm optimization Goose team optimization Role division Parallel principle Aggregate principle |
Issue Date | 2008 |
Publisher | Elsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/726366/description#description |
Citation | Progress in Natural Science: materials international, 2008, v. 18 n. 11, p. 1437-1443 How to Cite? |
Abstract | Referring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO, a team is formed by the particles with a leader and some followers. The role of the leader is to determine the search direction. The followers decide their flying modes according to their distances to the leader individually. Thus, a wide area can be explored and the particle collision can be really avoided. When GTO is applied to four benchmark examples of complex nonlinear functions, it has a better computation performance than the standard PSO. |
Persistent Identifier | http://hdl.handle.net/10722/222784 |
ISSN | 2023 Impact Factor: 4.8 2023 SCImago Journal Rankings: 1.025 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, J | - |
dc.contributor.author | Wang, D | - |
dc.date.accessioned | 2016-01-28T06:54:54Z | - |
dc.date.available | 2016-01-28T06:54:54Z | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | Progress in Natural Science: materials international, 2008, v. 18 n. 11, p. 1437-1443 | - |
dc.identifier.issn | 1002-0071 | - |
dc.identifier.uri | http://hdl.handle.net/10722/222784 | - |
dc.description.abstract | Referring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO, a team is formed by the particles with a leader and some followers. The role of the leader is to determine the search direction. The followers decide their flying modes according to their distances to the leader individually. Thus, a wide area can be explored and the particle collision can be really avoided. When GTO is applied to four benchmark examples of complex nonlinear functions, it has a better computation performance than the standard PSO. | - |
dc.language | eng | - |
dc.publisher | Elsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/726366/description#description | - |
dc.relation.ispartof | Progress in Natural Science: materials international | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Particle swarm optimization | - |
dc.subject | Goose team optimization | - |
dc.subject | Role division | - |
dc.subject | Parallel principle | - |
dc.subject | Aggregate principle | - |
dc.title | Particle swarm optimization with a leader and followers | - |
dc.type | Article | - |
dc.identifier.email | Wang, J: jwwang@hku.hk | - |
dc.identifier.authority | Wang, J=rp01888 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1016/j.pnsc.2008.03.029 | - |
dc.identifier.scopus | eid_2-s2.0-56849126666 | - |
dc.identifier.volume | 18 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 1437 | - |
dc.identifier.epage | 1443 | - |
dc.identifier.isi | WOS:000262137200015 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1002-0071 | - |