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Optimal layout and deployment for RFID system using a novel hybrid artificial bee colony optimizer based on bee life-cycle model
Jing, Shikai; Lian, Xiaodan; Chen HN(陈瀚宁); Zou T(邹涛); Ma LB(马连博)
Department工业控制网络与系统研究室
Source PublicationSoft Computing
ISSN1432-7643
2017
Volume21Issue:14Pages:4055-4083
Indexed BySCI ; EI
EI Accession number20160902030922
WOS IDWOS:000405500500019
Contribution Rank3
Funding OrganizationNational Natural Science Foundation of China under Grant Nos. 61503373, 61305028, 51205037, 61203161, and 51205389, and the Natural Science Foundation of Liaoning Province under Grant Nos. 2015020002, 201102200 and 201202226.
KeywordRfid Network Planning Hybrid Artificial Bee Colony Life-cycle Varying-population Distributed Decision Making
AbstractLarge-scale radio frequency identification (RFID) network planning (RNP) problem has been proven to be a NP-hard issue, which can be formulated as a high-dimensional nonlinear optimization problem with a mixture of discrete and continuous variables and uncertain parameters. First, a two-level optimization model for RFID network planning based on distributed decision making (DDM) is presented in this paper. In this model, the mixed discrete and continuous planning variables, namely the number, location, and radiate power of RFID readers are optimized. In each level of the optimization model, the different objectives to determine optimal values for these planning variables are as follows: (i) minimization of total installation cost of RFID network in the top-level; (ii) maximization of tag coverage and network reliability, and minimization of reader interference in the lower-level. In order to solve the proposed model effectively, this work proposes an efficient approach for RNP problem, namely the hybrid artificial bee colony optimizer (HABC), which employs the natural life-cycle mechanism to cast the original ABC framework to a cooperative and population varying fashion. In the proposed HABC, individuals can dynamically shift their survival states and population size varies dynamically according to the local fitness landscape during the executions of algorithm. These new characteristics of HABC help to avoid redundant search and maintain diversity of population in complex environments. Experiments are conducted on a set of CEC2005 and discrete benchmarks for evaluating the proposed algorithm. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS KeywordNUMERICAL FUNCTION OPTIMIZATION ; DIVISION-OF-LABOR ; PARTICLE SWARM ; ALGORITHM ; NETWORK ; CONSTRAINTS ; SEARCH
WOS Research AreaComputer Science
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/17704
Collection工业控制网络与系统研究室
Corresponding AuthorChen HN(陈瀚宁); Zou T(邹涛); Ma LB(马连博)
Affiliation1.School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 110081, China
2.School of Computer Science and Software, Tianjin Polytechnic University, Tianjin, 300387, China
3.Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Jing, Shikai,Lian, Xiaodan,Chen HN,et al. Optimal layout and deployment for RFID system using a novel hybrid artificial bee colony optimizer based on bee life-cycle model[J]. Soft Computing,2017,21(14):4055-4083.
APA Jing, Shikai,Lian, Xiaodan,Chen HN,Zou T,&Ma LB.(2017).Optimal layout and deployment for RFID system using a novel hybrid artificial bee colony optimizer based on bee life-cycle model.Soft Computing,21(14),4055-4083.
MLA Jing, Shikai,et al."Optimal layout and deployment for RFID system using a novel hybrid artificial bee colony optimizer based on bee life-cycle model".Soft Computing 21.14(2017):4055-4083.
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