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A novel particle swarm optimizer using optimal foraging theory
Niu B(牛奔); Zhu YL(朱云龙); Hu KY(胡琨元); Li SF(李素粉); He XX(何小贤)
Department先进制造技术实验室
Conference NameInternational Conference on Intelligent Computing (ICIC)
Conference DateAugust 16-19, 2006
Conference PlaceKunming, China
Author of SourceIEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China
Source PublicationCOMPUTATIONAL INTELLIGENCE AND BIOINFORMATICS, PT 3, PROCEEDINGS
PublisherSPRINGER-VERLAG
Publication PlaceBERLIN
2006
Pages61-71
Indexed BySCI ; EI ; CPCI(ISTP)
EI Accession number20064210172344
WOS IDWOS:000240085400007
Contribution Rank1
ISSN0302-9743
ISBN3-540-37277-6
AbstractBased on the research of optimal foraging theory (OFT), we present a novel particle swarm optimizer (PSO) to improve the performance of standard PSO (SPSO). The resulting algorithm is known as PSOOFT that makes use of two mechanisms of OFT: a reproduction strategy to enhance the ability to converge rapidly to good solutions and a patch-choice based scheme to keep a right balance of exploration and exploitation. In the simulation studies, several benchmark functions are performed, and the performance of the proposed algorithm is compared to the standard PSO (SPSO). The experimental results show that the PSOOFT prevents premature convergence to a high degree, but still has a more rapid convergence rate than SPSO.
Language英语
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/8158
Collection工业信息学研究室_先进制造技术研究室
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.School of Graduate, Chinese Academy of Sciences, Beijing, 100039, China
Recommended Citation
GB/T 7714
Niu B,Zhu YL,Hu KY,et al. A novel particle swarm optimizer using optimal foraging theory[C]//IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China. BERLIN:SPRINGER-VERLAG,2006:61-71.
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