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Wireless Networks
Published in: Wireless Networks 4/2019

07-11-2017

A new variant of cuckoo search algorithm with self adaptive parameters to solve complex RFID network planning problem

Authors: Atef Jaballah, Aref Meddeb

Published in: Wireless Networks | Issue 4/2019

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Abstract

With the paradigm of the Internet of things, each object in the physical world can be remotely identified, controlled, and located through networks. Thanks to their low cost and their small form, the Radio frequency identification (RFID) tags are frequently used to tag objects . The tags or objects are often distributed in large geographic areas. Due to the limit of the interrogation range of RFID readers, multiple readers should be deployed to read the information stored on all tags. The major challenge in an RFID network design is to find the optimal placement and parameters of readers in order to meet the essential requirements of an RFID system such as coverage, load balance and interference between readers. This challenge has led to a new research area known in the literature as the RFID network planning problem. This problem is characterized by a large number of constraints as well as numerous objectives and it proves to be NP-hard. In this paper, we develop a novel optimization algorithm, namely the self adaptive cuckoo search (SACS) algorithm, to solve this complex problem. In the SACS algorithm, the control parameters of the cuckoo search (CS) algorithm are adjusted dynamically in real time. The self-adaptation phenomenon allows the evolutionary algorithm to be more flexible and closer to natural evolution. The experimental results on 13 standard benchmark functions demonstrate that the proposed algorithm is more efficient than five adaptive variants of the CS algorithm. In the second part of the paper, the SACS algorithm is also used to solve three difficult RFID network planning instances. The simulation studies show that the SACS algorithm obtains better solutions for the RFID network planning problem than the original CS, four adaptive CS variants, the GA and the PSO in terms of optimization and robustness. To test the effectiveness of the SACS algorithm on a real problem, a case study is carried out.
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Literature
1.
Wang, J. (2014). RFID as a key enabler of the internet of things: Localization and communication. PhD thesis, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science., Massachusetts Institute of Technology.
2.
3.
Hanning, C., Yunlong, Z., Kunyuan, H., & Tao, K. (2011). RFID network planning using a multi-swarm optimizer. Journal of Network and Computer Applications, 34(3), 888–901.CrossRef
4.
Gong, Y., Shen, M., Zhang, J., Kaynak, O., Chen, W., & Zhan, Z. (2012). Optimizing RFID network planning by using a particle swarm optimization algorithm with redundant reader elimination. IEEE Transactions on Industrial Informatics, 8(4), 900–912.CrossRef
5.
Gao, Y., Hu, X., Liu, H., & Feng, Y. (2010). Multiobjective estimation of distribution algorithm combined with PSO for RFID network optimization. In 2010 international conference on measuring technology and mechatronics automation (ICMTMA), (pp. 736–739).
6.
7.
Nebojsa Bacanin, MT., & Strumberger, I. (2015). RFID network planning by ABC algorithm hybridized with heuristic for initial number and locations of readers. In 17th UKSIM-AMSS international conference on modelling and simulation, (pp. 39–44).
8.
Guan, Q., Liu, Y., Yang, Y., & Yu, W. (2006). Genetic approach for network planning in the RFID systems. In Sixth International Conference on Intelligent Systems Design and Applications, 2006. ISDA ’06 (Vol. 2, pp. 567–572).
9.
Yang, Y., Wu, Y., Xia, M., & Qin, Z. (2009). A RFID network planning method based on genetic algorithm. In International conference on networks security, wireless communications and trusted computing (pp. 534–537).
10.
Civicioglu, P., & Besdok, E. (2013). A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 39(4), 315–346.CrossRef
11.
Yang, X., & Deb, S. (2009). Cuckoo search via lévy flights. In World congress on nature and biologically inspired computing, NaBIC 2009, 9–11 December 2009, Coimbatore, India (Vol. 4, pp. 210–214).
12.
Yang, X., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330–343.CrossRefMATH
13.
Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35.CrossRef
14.
Chen, H., Zhu, Y., & Hu, K. (2010). Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning. Applied Soft Computing, 10(2), 539–547.CrossRef
15.
Ma, L., Hu, K., Zhu, Y., & Chen, H. (2014). Cooperative artificial bee colony algorithm for multi-objective RFID network planning. Journal of Network and Computer Applications, 42, 143–162.CrossRef
16.
Zhao, C., Wu, C., Chai, J., Wang, X., Yang, X., Lee, J. M., et al. (2017). Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty. Applied Soft Computing, 55, 549–564.CrossRef
17.
Lu, S., & Yu, S. (2014). A fuzzy k-coverage approach for RFID network planning using plant growth simulation algorithm. Journal of Network and Computer Applications, 39, 280–291.CrossRef
18.
Zhang, T., & Liu, J. (2017). An efficient and fast kinematics-based algorithm for RFID network planning. Computer Networks, 121, 13–24.CrossRef
19.
Tuba Milan, BM., & Bacanin Nebojsa (2015). Fireworks algorithm for RFID network planning problem. In 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA) (pp. 440–444).
20.
Bacanin Nebojsa, JR., & Tuba Milan (2015). Hierarchical multiobjective RFID network planning using firefly algorithm. In 2015 international conference on information and communication technology research (ICTRC) (pp 282–285).
21.
Tuba Milan, BN. (2015). Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning. In 2015 IEEE congress on evolutionary computation (CEC) (pp 499–506).
22.
Bhattacharya, I., & Roy, U. K. (2010). Optimal placement of readers in an RFID network using particle swarm optimization. International Journal of Computer Networks and Communications, 2(6), 225–234.CrossRef
23.
Yang, X., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing and Applications, 24(1), 169–174.CrossRef
24.
Shehab, M., Khader, A. T., & Al-Betar, M. A. (2017). A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing, 61, 1041–1059.
25.
Eiben, A., & Smit, S. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1), 19–31.CrossRef
26.
Gandino, F., Ferrero, R., Montrucchio, B., & Rebaudengo, M. (2013). Cuckoo search: A new nature-inspired optimization method for phase equilibrium calculations. Fluid Phase Equilibria, 337, 191–200.CrossRef
27.
Valian, E., Tavakoli, S., Mohanna, S., & Haghi, A. (2013). Improved cuckoo search for reliability optimization problems. Computers & Industrial Engineering, 64(1), 459–468.CrossRef
28.
Zhang, Z., & Chen, Y. (2014). An improved cuckoo search algorithm with adaptive method. In 2014 seventh international joint conference on computational sciences and optimization (CSO) (pp. 204–207).
29.
Zhao, H., Jiang, Y., Wang, T., Cui, W., & Li, X. (2016). A method based on the adaptive cuckoo search algorithm for endmember extraction from hyperspectral remote sensing images. Remote Sensing Letters, 7(3), 289–297.CrossRef
30.
Kumar, M. N., & Panda, R. (2016). A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Applied Soft Computing, 38, 661–675.CrossRef
31.
Jia, B., Yu, B., Wu, Q., Wei, C., & Law, R. (2016). Adaptive affinity propagation method based on improved cuckoo search. Knowledge-Based Systems, 111, 27–35.CrossRef
32.
33.
Katzela, I., & Naghshineh, M. (1996). Channel assignment schemes for cellular mobile telecommunication systems. IEEE Personal Communications, 3, 10–31.CrossRef
34.
Chen, H., Zhu, Y., Hu, K., & Niu, B. (2007). Application of a multi-species optimizer in ubiquitous computing for RFID networks scheduling. In Third international conference on natural computation (ICNC 2007) (pp. 420–425).
35.
Dong, Q., Shukla, A., Shrivastava, V., Agrawal, D., Banerjee, S., & Kar, K. (2007). Load balancing in large-scale RFID systems. In INFOCOM 2007. 26th IEEE international conference on computer communications. IEEE (pp. 2281–2285).
36.
Gandino, F., Ferrero, R., Montrucchio, B., & Rebaudengo, M. (2011). Probabilistic dcs: An RFID reader-to-reader anti-collision protocol. Journal of Network and Computer Applications, 34(3), 821–832.CrossRef
37.
Eom, J. B., Yim, S. B., & Lee, T. J. (2009). An efficient reader anticollision algorithm in dense RFID networks with mobile RFID readers. IEEE Transactions on Industrial Electronics, 56(7), 2326–2336.CrossRef
Metadata
Title
A new variant of cuckoo search algorithm with self adaptive parameters to solve complex RFID network planning problem
Authors
Atef Jaballah
Aref Meddeb
Publication date
07-11-2017
Publisher
Springer US
Published in
Wireless Networks / Issue 4/2019
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-017-1616-9

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