Hierarchical artificial bee colony algorithm for RFID network planning optimization.
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations fr...
| Publicado en: | Scientific World Journal pp. 941532 - 941533 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104037472&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104037472 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104037472 NLM24592200 2012496155 10.1155/2014/941532 NLM24592200 PMC3921959 104037472 ppf: 941532 ppct: 1 formats: tig: atl: Hierarchical artificial bee colony algorithm for RFID network planning optimization. aug: au: Ma, Lianbo Chen, Hanning Hu, Kunyuan Zhu, Yunlong affil: Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office VII, Nanta Street No. 114, Dongling District, Shenyang 110016, China ; University of Chinese Academy of Sciences, Beijing 100039, China. sug: subj: Algorithms Bees and Wasps Physiology Models, Biological Radio Frequency Identification Methods Social Behavior Animal Studies ab: This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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