Soft Computing

, Volume 21, Issue 14, pp 4055–4083 | Cite as

Optimal layout and deployment for RFID system using a novel hybrid artificial bee colony optimizer based on bee life-cycle model

Methodologies and Application
First Online:

Abstract

Large-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.

Keywords

RFID network planning Hybrid artificial bee colony Life-cycle Varying-population Distributed decision making 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This research is partially supported by National 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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Basturk B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142CrossRefGoogle Scholar
  2. Beshers SN, Huang ZY, Oonoa Y, Robinson GE (2001) Social Inhibition and the regulation of temporal polyethism in honey bees. J Theor Biol 213:461–479CrossRefGoogle Scholar
  3. Bhattacharya I, Roy UK (2010) Optimal placement of readers in an RFID network using particle swarm optimization. Int J Comput Netw Commun 2(6):225–234Google Scholar
  4. Carbunar B, Ramanathan MK, Koyut M et al. (2009) Efficient tag detection in RFID systems. J Parallel Distrib Comput 69:180–196Google Scholar
  5. Chang TH, Hsu SC, Wang TC (2013) A proposed model for measuring the aggregative risk degree of implementing an RFID digital campus system with the consistent fuzzy preference relations. Appl Math Model 37(5):2605–2622MathSciNetCrossRefMATHGoogle Scholar
  6. Changhe L (2010) Particle swarm optimization in stationary and dynamic environments, University of LeicesterGoogle Scholar
  7. Chen HN, Zhu YL, Hu KY (2010) Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning. Appl Soft Comput 10(2):539–547CrossRefGoogle Scholar
  8. Chen HN, Zhu YL, Hu KY (2010) Virtual enterprise risk management using artificial intelligence. Math Prob Eng 2010:1–20MATHGoogle Scholar
  9. Chen HN, Zhu YL, Hu KY, Ku T (2011) RFID network planning using a multi-swarm optimizer. J Netw Comput Appl 34(3):888–901CrossRefGoogle Scholar
  10. Clerc M (2005) Binary particle swarm optimizers: toolbox, derivations, and mathematical insights. http://clerc.maurice.free.fr/pso/
  11. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  12. Cox MD, Myerscough MR (2003) A flexible model of foraging by a honey bee colony: the effects of individual behaviour on foraging success. J Theor Biol 223:179–197MathSciNetCrossRefGoogle Scholar
  13. DeGrandi-Hoffman G, Roth SA, Loper GL et al (1989) BEEPOP: a honeybee population dynamics simulation model. Ecol Model 45:133–150CrossRefGoogle Scholar
  14. Derrac J, García S, Molina D et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18CrossRefGoogle Scholar
  15. Dobkin DM (2004) The RF in RFID: passive UHF RFID in practice. Elsevier, pp 68–77Google Scholar
  16. Dong A, Shukla V, Shrivastava D (2008) Agrawal, load balancing in large-scale RFID systems. Comput Netw 52(9):1782–1796CrossRefMATHGoogle Scholar
  17. Engels WS, Sarma E (2002) The reader collision problem. In: Proceeding of the 2002 IEEE international conference on systems, man and cybernetics, pp 370–76Google Scholar
  18. Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Glob Optim 6:109–133MathSciNetCrossRefMATHGoogle Scholar
  19. Gao Y, Hu X, Liu HL, et al (2010) Multiobjective estimation of distribution algorithm combined with PSO for RFID network optimization. In: Proceedings of 2010 International Conference on Measuring Technology and Mechatronics Automation, pp 736–739Google Scholar
  20. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search, Simulation 76(2):60–68Google Scholar
  21. Glover F, Taillard E, de Werra D (1993) A user’s guide to tabu search. Ann Oper Res 41(1):3–28CrossRefMATHGoogle Scholar
  22. Gong YJ, Shen M, Zhang J, Kaynak O, Chen WN, Zhan ZH (2012) Optimizing RFID Network planning by using a particle swarm optimization algorithm with redundant reader elimination. IEEE Trans Ind Inf 8(4):900–912CrossRefGoogle Scholar
  23. Guan Q, Liu Y, Yang YP, Yu WS (2013) Genetic approach for network planning in the RFID systems. Proc Sixth Int Conf Intel Syst Des Appl 37(10–11):6758–6779Google Scholar
  24. Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195Google Scholar
  25. Homberger J, Gehring H (2001) A parallel two-phase metaheuristic for routing problems with time windows. Asia-Pacific J Oper Res 13(1):35–47MATHGoogle Scholar
  26. Homberger J, Gehring H (2005) A two-phase hybrid metaheuristic for the vehicle routing problem with time windows. Eur J Oper Res 162(1):220–238CrossRefMATHGoogle Scholar
  27. Huang ZY, Robinson GE (1992) Honeybee colony integration: worker-worker interactions mediate hormonally regulated plasticity in division of labor. Proc Natl Acad Sci USA 89:11726–11729CrossRefGoogle Scholar
  28. Huang ZY, Robinson GE (1996) Regulation of honey bee division of labor by colony age demography. Behav Ecol Sociobiol 39:147–158CrossRefGoogle Scholar
  29. Islam SM, Das S, Ghosh S, Roy S, Suganthan PN (2012) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst Man Cybernet Part B. Cybernet 42(2):482–500Google Scholar
  30. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Glob Optim 39(3):459–471MathSciNetCrossRefMATHGoogle Scholar
  31. Khoury DS, Myerscough MR, Barron AB (2011) A quantitative model of honeybee colony population dynamics. PLoS ONE 6:e18491CrossRefGoogle Scholar
  32. Li MS, Ji TY, Tang WJ, Wu QH, Saunders JR (2010) Bacterial foraging algorithm with varying population. BioSystems 100:185–197CrossRefGoogle Scholar
  33. Li Z, Li J, He C (2014) Artificial immune network-based anti-collision algorithm for dense RFID readers. Expert Syst Appl 41(10):4798–4810CrossRefGoogle Scholar
  34. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization ofmultimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRefGoogle Scholar
  35. Liu W, Niu B, Chen H et al (2013) Artificial Bee colony algorithm for reader collision problem in radio frequency identification network. J Comput Theoretical Nanosci 10(12):2877–2883CrossRefGoogle Scholar
  36. Ma L, Chen H, Hu K et al (2014) Hierarchical artificial bee colony algorithm for RFID network planning optimization. Sci World J 2014:1–21Google Scholar
  37. Oster GF, Wilson EO (1978) Caste and Ecology in the Social Insects, Princeton. Princeton University Press, NJGoogle Scholar
  38. Rashidi H, Tsang PK (2013) Novel constraints satisfaction models for optimization problems in container terminals. Appl Math Model 37(6):3601–3634CrossRefGoogle Scholar
  39. Seok JH, Lee JY, Oh C, Lee JJ, et al (2010) RFID sensor deployment using differential evolution for indoor mobile robot localization. In: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp 3719–3724Google Scholar
  40. Solomon M (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35(2):254–265MathSciNetCrossRefMATHGoogle Scholar
  41. Sumathi S, Hamsapriya T, Surekha P (2008) Evolutionary intelligence: an introduction to theory and applications with Matlab. SpringerGoogle Scholar
  42. Tan J, Kong F, Liang W (2013) A 3D object model for wireless camera networks with network constraints. Trans Inst Meas Control 35(7):866–874CrossRefGoogle Scholar
  43. Tasgetiren MF, Pan QK, Suganthan PN (2006) A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion. Appl Math Model 2:567–572Google Scholar
  44. Wang YC, Tseng YC (2008) Distributed deployment schemes for mobile wireless sensor networks to ensure multilevel coverage. IEEE Trans Parallel Distrib Syst 19(9):1280–1294CrossRefGoogle Scholar
  45. Wolpert DH, Macready WG (1997) No free lunch theorems for search. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  46. Yang XS, Deb S (2009) Cuckoo search via L’evy flights. In: Proc. of world congress on nature and biologically inspired computing (NaBic 2009), IEEE Publications, USA, pp 210–214Google Scholar
  47. Yang YH, Wu YJ, Xia M, Qin ZJ (2009) A RFID network planning method based on genetic algorithm. In: Proc. 2009 Int. Conf. Netw. Security, Wireless Commun. Trusted Comput. (NSWCTC 2009), vol. 1, pp 534–537Google Scholar
  48. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Num Optim 1:330–343MATHGoogle Scholar
  49. Zhu GP, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Mechanical EngineeringBeijing Institute of TechnologyBeijingChina
  2. 2.School of Computer Science and SoftwareTianjin Polytechnic UniversityTianjinChina
  3. 3.Laboratory of Information Service and Intelligent Control, Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina

Personalised recommendations