Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning

被引:86
作者
Chen, Hanning [1 ]
Zhu, Yunlong [1 ]
Hu, Kunyuan [1 ]
机构
[1] Chinese Acad Sci, Shenyang Inst Automat, Key Lab Ind Informat, Fac Off 3, Shenyang 110016, Peoples R China
关键词
RFID network planning; Bacteria Foraging Algorithm; MC-BFO; GA; PSO; CHEMOTAXIS;
D O I
10.1016/j.asoc.2009.08.023
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In order to obtain accurate and reliable network planning in the Radio Frequency Identification (RFID) communication system, the locations of readers and the associated values for each of the reader parameters have to be determined. All these choices must optimize a set of objectives, such as tag coverage, economic efficiency, load balance, and interference level between readers. This paper proposes a novel optimization algorithm, namely the multi-colony bacteria foraging optimization (MC-BFO), to solve complex RFID network planning problem. The main idea of MC-BFO is to extend the single population bacterial foraging algorithm to the interacting multi- colony model by relating the chemotactic behavior of single bacterial cell to the cell-to-cell communication of bacterial community. With this multi- colony cooperative approach, a suitable diversity in the whole bacterial community can be maintained. At the same time, the cell-to-cell communication mechanism significantly speeds up the bacterial community to converge to the global optimum. Then a mathematical model for planning RFID networks is developed based on the proposed MC-BFO. The performance of MC-BFO is compared to both GA and PSO on RFID network planning problem, demonstrating its superiority. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:539 / 547
页数:9
相关论文
共 28 条
[1]   CHEMOTAXIS IN BACTERIA [J].
ADLER, J .
SCIENCE, 1966, 153 (3737) :708-&
[2]   Bacterial linguistic communication and social intelligence [J].
Ben Jacob, E ;
Becker, I ;
Shapira, Y ;
Levine, H .
TRENDS IN MICROBIOLOGY, 2004, 12 (08) :366-372
[3]  
BERG HC, 1972, NATURE, V239, P500, DOI 10.1038/239500a0
[4]  
Bonabeau E., 1999, Swarm Intelligence: From Natural to Artificial Systems
[5]   Base-station network planning including environmental impact control [J].
Cerri, G ;
De Leo, R ;
Micheli, D ;
Russo, P .
IEE PROCEEDINGS-COMMUNICATIONS, 2004, 151 (03) :197-203
[6]  
CHEN HN, 2007, P 3 INT C NAT COMP H, V2, P420
[7]   The evolution of social behavior in microorganisms [J].
Crespi, BJ .
TRENDS IN ECOLOGY & EVOLUTION, 2001, 16 (04) :178-183
[8]  
Decker C, 2003, 23RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS, P328
[9]   Load balancing in large-scale RFID systems [J].
Dong, Qunfeng ;
Shukla, Ashutosh ;
Shrivastava, Vivek ;
Agrawal, Dheeraj ;
Banerjee, Suman ;
Kar, Koushik .
COMPUTER NETWORKS, 2008, 52 (09) :1782-1796
[10]  
Eberhart R.C., 2001, Swarm Intelligence