A self learned invasive weed-mixed biogeography based optimization algorithm for RFID network planning


The optimal placement of the RFID readers inaugurates an ongoing research field, namely the RFID network planning (RNP). The main issue in the RNP is to know how many readers have to be used and what is their best distribution that guarantees fulfillment of multiple objectives. The common RNP objectives are the optimal coverage, readers’ interference avoidance, redundant reader elimination, load balance among deployed readers and minimum power losses, which are considered as conflicting objectives that leads the RNP to be an NP-hard problem need to be solved. The contributions in this paper are: firstly, utilizing both the Biogeography based optimization (BBO) and the Hybrid Invasive Weed-Biogeography based optimization (HIW-BBO) as new algorithms have not used before for solving the RNP. Secondly, we proposed a Self Learning (SL) strategy with a mixed BBO Migration (MBBOM) operation to modify the HIW-BBO algorithm in an algorithm called Self Learned Invasive Weed-Mixed Biogeography based optimization (SLIWMBBO). Thirdly, the performance of the proposed SLIWMBBO algorithm is compared to both the HIW-BBO and the Self Adaptive Cuckoo Search (SACS) optimization algorithms according to a set of 13 benchmark functions. The results of this comparison encourage the application of the SLIWMBBO as an optimization algorithm for solving the complex problems. Lastly, the BBO, HIW-BBO and SLIWMBBO optimization algorithms are used for solving three complex RNP instances and compared to the SACS algorithm. Simulation results of the SLIWMBBO are outstanding and demonstrate its superiority over the compared algorithms.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Finkenzeller, K. (2010). RFID handbook: Fundamentals and applications in contactless smart cards, radio frequency identification and near-field communication. New York: Wiley.

    Google Scholar 

  2. 2.

    Liu, N., et al. (2015). Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity. Wireless Networks,21(3), 841–857.

    Article  Google Scholar 

  3. 3.

    Rezaie, H., & Golsorkhtabaramiri, M. (2018). A fair reader collision avoidance protocol for RFID dense reader environments. Wireless Networks,24(6), 1953–1964.

    Article  Google Scholar 

  4. 4.

    Golsorkhtabaramiri, M., et al. (2018). Comparison of energy consumption for reader anti-collision protocols in dense RFID networks. Wireless Networks,1, 1–14.

    Google Scholar 

  5. 5.

    Niu, B., et al. (2009). RFID Network Planning Based on MCPSO Alogorithm. In 2009 Second international symposium on information science and engineering (ISISE). IEEE.

  6. 6.

    Fister Jr, I., et al. (2013). A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186.

  7. 7.

    Kar, A. K. (2016). Bio inspired computing–A review of algorithms and scope of applications. Expert Systems with Applications,59, 20–32.

    Article  Google Scholar 

  8. 8.

    Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation,214(1), 108–132.

    MathSciNet  MATH  Article  Google Scholar 

  9. 9.

    Lewis, A., et al. (2009) Optimising efficiency and gain of small meander line RFID antennas using ant colony system. In 2009 IEEE Congress on Evolutionary Computation. IEEE.

  10. 10.

    Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation,12(6), 702–713.

    Article  Google Scholar 

  11. 11.

    Gong, W., Cai, Z., & Ling, C. X. (2010). DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Computing,15(4), 645–665.

    Article  Google Scholar 

  12. 12.

    Rashid, A., et al. (2016). A dynamic oppositional biogeography-based optimization approach for time-varying electrical impedance tomography. Physiological Measurement,37(6), 820.

    MathSciNet  Article  Google Scholar 

  13. 13.

    Rahmati, S. H. A., & Zandieh, M. (2011). A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology,58(9–12), 1115–1129.

    Google Scholar 

  14. 14.

    Zahran, E. G., et al. (2019). Biogeography based optimization algorithm for efficient RFID reader deployment. In Proceedings of the 2018 13th International Conference on Computer Engineering and Systems, ICCES 2018.

  15. 15.

    Ma, H., Fei, M., & Yang, Z. (2016). Biogeography-based optimization for identifying promising compounds in chemical process. Neurocomputing,174, 494–499.

    Article  Google Scholar 

  16. 16.

    Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation,1(1), 67–82.

    Article  Google Scholar 

  17. 17.

    Hordri, N., Yuhaniz, S., & Nasien, D. (2013). A comparison study of biogeography based optimization for optimization problems. International Journal of Advances in Soft Computing and its Applications,5, 1–16.

    Google Scholar 

  18. 18.

    Khademi, G., Mohammadi, H., & Simon, D. (2017). Hybrid invasive weed/biogeography-based optimization. Engineering Applications of Artificial Intelligence,64, 213–231.

    Article  Google Scholar 

  19. 19.

    Montgomery, J., Randall, M., & Lewis, A. (2011) Differential evolution for RFID antenna design: A comparison with ant colony optimisation. In Proceedings of the 13th annual conference on Genetic and evolutionary computation. ACM.

  20. 20.

    Guan, Q., et al. (2006). Genetic approach for network planning in the RFID systems. In 6th International conference on intelligent systems design and applications. IEEE.

  21. 21.

    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.

    Article  Google Scholar 

  22. 22.

    Gao, Y., et al. (2010). Multiobjective estimation of distribution algorithm combined with PSO for RFID network optimization. In 2010 International conference on measuring technology and mechatronics automation. IEEE.

  23. 23.

    Bacanin, N., M. Tuba, & Strumberger, I. (2015). RFID network planning by ABC algorithm hybridized with heuristic for initial number and locations of readers. In Proceeding of the 17th UKSIM-AMSS international conference on modeling and simulation.

  24. 24.

    Jaballah, A., & Meddeb, A. (2017). A new variant of cuckoo search algorithm with self adaptive parameters to solve complex RFID network planning problem. Wireless Networks,1, 1–20.

    Google Scholar 

  25. 25.

    Kim, J., et al. (2006) Effect of localized optimal clustering for reader anti-collision in RFID networks: fairness aspects to the readers. In Proceedings. 14th International conference on computer communications and networks, ICCCN 2005.

  26. 26.

    Leong, K. S., Ng, M. L., & Cole P. H. (2006). Positioning analysis of multiple antennas in a dense RFID reader environment. In International symposium on applications and the internet workshops (SAINTW’06). IEEE.

  27. 27.

    Bhattacharya, I., & Roy, U. K. (2010). Optimal placement of readers in an RFID network using particle swarm optimization. International Journal of Computer Networks & Communications,2(6), 225–234.

    Article  Google Scholar 

  28. 28.

    Chen, H., et al. (2011). RFID network planning using a multi-swarm optimizer. Journal of Network and Computer Applications,34(3), 888–901.

    Article  Google Scholar 

  29. 29.

    Chen, H., et al. (2011). Dynamic RFID network optimization using a self-adaptive bacterial foraging algorithm. International Journal of Artificial Intelligence.,2011(7), 219–231.

    Google Scholar 

  30. 30.

    Chen, H., et al. (2014). Multiobjective RFID network optimization using multiobjective evolutionary and swarm intelligence approaches. Mathematical Problems in Engineering,2014, 1.

    Google Scholar 

  31. 31.

    Ma, L., et al. (2014). Hierarchical artificial bee colony algorithm for RFID network planning optimization. The Scientific World Journal,2014, 1.

    Google Scholar 

  32. 32.

    Tang, L., et al. (2016). Uncertainty-aware RFID network planning for target detection and target location. Journal of Network and Computer Applications,74, 21–30.

    Article  Google Scholar 

  33. 33.

    Elewe, A. M., Hasnan, K., & Nawawi, A. (2017). Optimization of RFID Network Planning Using MDB-FA Method. Journal of Telecommunication Electronic and Computer Engineering (JTEC),9(2–12), 7–12.

    Google Scholar 

  34. 34.

    Raghib, A., et al. (2017) Robustness optimization for solving the deployment of RFID readers problem. In Proceedings of the international conference on multimedia computing and systems.

  35. 35.

    Gunawan, S., & Azarm, S. (2005). Multi-objective robust optimization using a sensitivity region concept. Structural and Multidisciplinary Optimization,29(1), 50–60.

    Article  Google Scholar 

  36. 36.

    Jing, S., et al. (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.

    Article  Google Scholar 

  37. 37.

    Zhang, T., & Liu, J. (2017). An efficient and fast kinematics-based algorithm for RFID network planning. Computer Networks,121, 13–24.

    Article  Google Scholar 

  38. 38.

    Zakeri, F., Golsorkhtabaramiri, M., & Hosseinzadeh, M. (2017). Optimizing radio frequency identification networks planning by using particle swarm optimization algorithm with fuzzy logic controller and mutation. IETE Journal of Research,63(5), 728–735.

    Article  Google Scholar 

  39. 39.

    Tsai, C. Y., Chang, H. T., & Kuo, R. J. (2017). An ant colony based optimization for RFID reader deployment in theme parks under service level consideration. Tourism Management,58, 1–14.

    Article  Google Scholar 

  40. 40.

    Elewe, A. M., Hasnan, K. B., & Nawawi, A. B. (2017). Hybridized firefly algorithm for multi-objective Radio Frequency Identification (RFID) Network planning. ARPN Journal of Engineering and Applied Sciences,12(3), 834–840.

    Google Scholar 

  41. 41.

    Strumberger, I., et al. ()2018. Modified monarch butterfly optimization algorithm for RFID network planning. In 2018 6th International conference on multimedia computing and systems (ICMCS). IEEE.

  42. 42.

    Ma, L., et al. (2019). Two-level master-slave rfid networks planning via hybrid multiobjective artificial bee colony optimizer. IEEE Transactions on Systems, Man, and Cybernetics: Systems,49(5), 861–880.

    Article  Google Scholar 

  43. 43.

    Azizi, A. (2019) Hybrid artificial intelligence optimization technique. In springerbriefs in applied sciences and technology (pp. 27–47).

  44. 44.

    Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation,274, 292–305.

    MathSciNet  MATH  Article  Google Scholar 

  45. 45.

    Sayah, S., & Hamouda, A. (2013). A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Applied Soft Computing,13(4), 1608–1619.

    Article  Google Scholar 

  46. 46.

    Qi, C., Gong, G., & Engels, D. (2012) How to develop clairaudience—Active eavesdropping in passive RFID systems. In IEEE international symposium on a world of wireless, mobile and multimedia networks, WoWMoM 2012digital proceedings. 2012.

  47. 47.

    Kim, D.-Y., et al. (2009). Effects of reader-to-reader interference on the UHF RFID interrogation range. IEEE Transactions on Industrial Electronics,56(7), 2337–2346.

    Article  Google Scholar 

  48. 48.

    Shrivastava, Q.D.A.S.V., et al. (2006) Load balancing in large-scale RFID systems.

  49. 49.

    Carbunar, B., et al. (2005). Redundant-reader elimination in RFID systems.

  50. 50.

    Ma, L., et al. (2014). Cooperative artificial bee colony algorithm for multi-objective RFID network planning. Journal of network and computer applications,42, 143–162.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to A. A. Arafa.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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 Netw 26, 4109–4127 (2020). https://doi.org/10.1007/s11276-020-02316-0

Download citation


  • BBO
  • Coverage
  • Load balance
  • Reader collision
  • RFID network planning (RNP) problem