|
[1] CARBUNAR B, RAMANATHAN
M K, KOYUTURK M, et al. Efficient tag detection in
RFID systems. Journal of Parallel and Distributed Computing,
2009, 69(2): 180 -196.
[2] CHANG T H, HSU S C,
WANG T C. A proposed model for measuring the aggregative
risk degree of implementingan RFID digital campus system with the
consistent fuzzy preference relations. Applied Mathematical Modelling,
2013, 37(5): 2605 -2622.
[3] TAN J D, KONG F Y,
LIANG W. A 3D object model for wireless camera networks with
network constraints. Transactions of the Institute of Measurement
and Control, 2009, 35(7): 866 -874.
[4] ZHONG R Y, HUANG G Q,
LAN S L, et al. A two-level advanced production
planning and scheduling model for RFID- enabled ubiquitous
manufacturing. Advanced Engineering Informatics, 2015, 29(4):
799 -812.
[5] YANG X S. Swarm
intelligence based algorithms: a critical analysis. Evolutionary
Intelligence, 2014, 7(1): 17 -28.
[6] GONG Y J, SHEN M,
ZHANG J, et al. Optimizing RFID network planning by using a
particle swarm optimization algorithm with redundant reader
elimination. IEEE Transactions on Industrial Informatics, 2012, 8(4):
900 -912.
[7] CHEN H N, ZHU Y L, HU
K Y, et al. RFID network planning using a multi-swarm
optimizer. Journal of Network and Computer Applications, 2011, 34(3):
888 -901.
[8] MORADI M H, ABEDINI M.
A combination of genetic algorithm and particle swarm
optimization for optimal distributed generation location and sizing in distribution
systems with fuzzy optimal theory. International Journal of
Green Energy, 2012, 9(7): 641 -660.
[9] GUAN Q, LIU Y, YANG Y
P, et al. Genetic approach for network planning in the
RFID systems. Proceedings of the 6th International Conference
on Intelligent Systems Design and Applications: Vol 2, 2006,
Oct 16 - 18, Ji’an, China. Piscataway, NJ, USA: IEEE,
2006: 567 -572.
[10] YANG Y H, WU Y J, XIA
M, et al. A RFID network planning method based on genetic
algorithm. Proceedings of the 2009 International Conference
on Networks Security, Wireless Communications and Trusted
Computing, 2009, Apr 25 - 26, Wuhan, China. Piscataway,
NJ, USA: IEEE, 2009: 534 -537.
[11] CHEN H N, ZHU Y L.
RFID networks planning using evolutionary algorithms
and swarm intelligence. Proceedings of the 4th International
Conference on Wireless Communications, Networking and Mobile
Computing, 2008,Oct 12 - 14, Dalian, China. Piscataway, NJ,
USA: IEEE, 2008: 1 -4.
[12] MA L B, HU K Y, ZHU Y
L, et al. Cooperative artificial bee colony algorithm for multi-objective
RFID network planning. Journal of Network and Computer
Applications, 2014, 42: 143 -162.
[13] YUAN C C, CHEN H N,
SHEN J, et al. Indicator-based multi- objective adaptive
bacterial foraging algorithm for RFID network planning. Cluster
Computing, 2019, 22(5): 12649 -12657.
[14] ZAHRAN E G, ARAFA A
A, SALEH H I, et al. A self learned invasive weed-mixed
biogeography based optimization algorithm for RFID network planning.
Wireless Networks, 2020, 26 ( 6 ): 4109 -4127.
[15] LU S L, YU S Z. A
fuzzy k-coverage approach for RFID network planning using plant
growth simulation algorithm. Journal of Network and Computer
Applications 2014, 39: 280 -291.
[16] MA L B, WANG X W,
HUANG M, et al. Two-level master-slave RFID networks planning via
hybrid multiobjective artificial bee colony optimizer. IEEE
Transactions on Systems, Man, and Cybernetics: Systems,
2017, 49(5): 861 -880.
[17] ZHAN Z H, ZHANG J,
SHI Y H, et al. A modified brain storm optimization. Proceedings
of the 2012 IEEE Congress on Evolutionary Computation,
2012, Jun 10 - 15, Brisbane, Australia. Piscataway, NJ,
USA: IEEE, 2012: 1 -8.
[18] CHEN J F, CHENG S,
CHEN Y, et al. Enhanced brain storm optimization algorithm for
wireless sensor networks deployment. Proceedings of the 6th
International Conference in Swarm Intelligence on Advances
in Swarm and Computational Intelligence (ICSI’15), 2015, Jun 25 - 28, Beijing, China. LNTCS 9140. Berlin, Germany: Springer,
2015: 373 -381.
[19] SHEN Y, YANG J, CHENG
S, et al. BSO-AL: brain storm optimization algorithm
with adaptive learning strategy. Proceedings of the 2020 IEEE Congress
on Evolutionary Computation (CEC’20), 2020,Jul 19 - 24, Glasgow, UK. Piscataway, NJ, USA: IEEE, 2020: 1 -7.
[20] YANG Z S, SHI Y H.
Brain storm optimization with chaotic operation. Proceedings of
the 7th International Conference on Advanced Computational
Intelligence ( ICACI’15), 2015, Mar 27 -29, Wuyi, China.
Piscataway, NJ, USA: IEEE, 2015: 111 -115.
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