References

• 1. 1)
     • 60. Guo, W., Chen, M., Wang, L., et al: ‘A survey of biogeography-based optimization’, Neural Comput. Appl., 2016, 11, pp. 1–18.
  2. 2)
     • 25. Boussaid, I., Chatterjee, A., Siarry, P., et al: ‘Hybridizing biogeography-based optimization with differential evolution for optimal power allocation in wireless sensor networks’, IEEE Trans. Veh. Technol., 2011, 60, pp. 2347–2353.
  3. 3)
     • 31. Wang, L., Fu, X., Mao, Y., et al: ‘A novel modified binary differential evolution algorithm and its applications’, Neurocomputing, 2012, 98, pp. 55–75.
  4. 4)
     • 51. Ma, H., Simon, D.: ‘Blended biogeography-based optimization for constrained optimization’, Eng. Appl. Artif. Intell., 2011, 24, pp. 517–555.
  5. 5)
     • 14. Ghasemi, A., Jahromi, A.F., Masnadi-Shirazi, M.A., et al: ‘Spectrum allocation based on artificial bee colony in cognitive radio networks’. 6th Int. Symp. Telecommunications, 2012, pp. 182–187.
  6. 6)
     • 33. Le, L.B., Hossain, E.: ‘Resource allocation for spectrum underlay in cognitive radio networks’, IEEE Trans. Wirel. Commun., 2008, 7, pp. 5306–5315.
  7. 7)
     • 54. Dib, N., Sharaqa, A.: ‘Design of non-uniform concentric circular antenna arrays with optimal sidelobe level reduction using biogeography-based optimization’, Int. J. Microw. Wirel. Technol., 2015, 7, pp. 161–166.
  8. 8)
     • 38. Raychaudhuri, D., Jing, X., Seskar, I., et al: ‘Cognitive radio technology: from distributed spectrum coordination to adaptive network collaboration’, Pervasive Mob. Comput., 2008, 4, pp. 278–302.
  9. 9)
     • 36. Letaief, K.B., Zhang, W.: ‘Cooperative communications for cognitive radio networks’, Proc. IEEE, 2009, 97, pp. 878–893.
  10. 10)
      • 29. Ma, H., Simon, D., Siarry, P., et al: ‘Biogeography-based optimization: a 10-year review’, IEEE Trans. Emerging Top. Comput. Intell., 2017, 1, pp. 391–407.
  11. 11)
      • 41. Haupt, R.L., Haupt, S.E.: ‘Practical genetic algorithms’ (Wiley and Sons, New York, 1998).
  12. 12)
      • 48. Crawford, B., Soto, R., Olivares-Suarez, M., et al: ‘A binary firefly algorithm for the set covering problem’. Modern Trends and Techniques in Computer Science: 3rd Computer Science On-line Conf., 2014, pp. 65–73.
  13. 13)
      • 46. Storn, R., Price, K.: ‘A simple and efficient heuristic for global optimization over continuous spaces’, J. Glob. Optim., 1997, 11, pp. 341–359.
  14. 14)
      • 34. Tang, J., Misra, S., Xue, G.: ‘Joint spectrum allocation and scheduling for fair spectrum sharing in cognitive radio wireless networks’, Comput. Netw., 2008, 52, pp. 2148–2158.
  15. 15)
      • 44. Dorigo, M., Maniezzo, V., Colorni, A.: ‘Ant system: optimization by a colony of cooperating agents’, IEEE Trans. Syst. Man Cybern. Part B: Cybern., 1996, 26, pp. 29–41.
  16. 16)
      • 11. Cao, L., Zheng, H.: ‘Distributed spectrum allocation via local bargaining’. Second Annual IEEE Communications Society Conf. Sensor and Ad Hoc Communications and Networks, 2005, pp. 475–486.
  17. 17)
      • 42. Haupt, R.L., Werner, D.H.: ‘Genetic algorithms in electromagnetics’ (Wiley-Interscience, Hoboken, 2007).
  18. 18)
      • 6. Li, X., Hu, F., Zhang, H., et al: ‘Two-branch wavelet denoising for accurate 400 spectrum sensing in cognitive radios’, Telecommun. Syst., 2014, 57, pp. 81–90.
  19. 19)
      • 12. Peng, C., Zheng, H., Zhao, B.Y.: ‘Utilization and fairness in spectrum assignment for opportunistic spectrum access’, Mobile Netw. Appl., 2006, 11, pp. 555–576.
  20. 20)
      • 45. Dorigo, M., Stutzle, T.: ‘Ant colony optimization’ (The MIT Press, Cambridge, MA, 2004).
  21. 21)
      • 28. Goudos, S.K., Baltzis, K.B., Siakavara, K., et al: ‘Reducing the number of elements in linear arrays using biogeography- based optimization’. 6th European Conf. Antennas and Propagation, 2012, pp. 1615–1618.
  22. 22)
      • 27. Bhattacharya, A., Chattopadhyay, P.K.: ‘Biogeography-based optimization for different economic load dispatch problems’, IEEE Trans. Power Syst., 2010, 25, pp. 1064–1077.
  23. 23)
      • 50. Ma, H., Simon, D.: ‘Analysis of migration models of biogeography-based optimization using Markov theory’, Eng. Appl. Artif. Intell., 2011, 24, pp. 1052–1060.
  24. 24)
      • 17. Abbas, N., Nasser, Y., Ahmad, K.E.: ‘Recent advances on artificial intelligence and learning techniques in cognitive radio networks’, EURASIP J. Wirel. Commun. Netw., 2015, 1, pp. 174–174.
  25. 25)
      • 57. Sharaqa, A., Dib, N.: ‘Design of linear and elliptical antenna arrays using biogeography based optimization’, Arab. J. Sci. Eng., 2014, 39, pp. 2929–2939.
  26. 26)
      • 37. Yuan, Y., Bahl, P., Chandra, R., et al: ‘Allocating dynamic time-spectrum blocks in cognitive radio networks’. Proc. 8th ACM Int. Symp. Mobile Ad Hoc Networking and Computing, 2007, pp. 130–139.
  27. 27)
      • 35. Mwangoka, J.W., Letaief, K.B., Cao, Z.: ‘Joint power control and spectrum allocation for cognitive radio networks via pricing’, Phys. Commun., 2009, 2, pp. 103–115.
  28. 28)
      • 21. Rathi, A., Agarwal, A., Sharma, A., et al: ‘A new hybrid technique for solution of economic load dispatch problems based on biogeography based optimization’. TENCON 2011 – 2011 IEEE Region 10 Conf., 2011, pp. 19–24.
  29. 29)
      • 49. Derrac, J., Garcia, S., Molina, D., et al: ‘A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms’, Swarm Evol. Comput., 2011, 1, pp. 3–18.
  30. 30)
      • 19. Simon, D.: ‘Biogeography-based optimization’, IEEE Trans. Evol. Comput., 2008, 12, pp. 702–713.
  31. 31)
      • 2. Pandit, S., Singh, G.: ‘Throughput maximization with reduced data loss rate in cognitive radio network’, Telecommun. Syst., 2013, 57, pp. 209–215.
  32. 32)
      • 23. Ashrafinia, S., Pareek, U., Naeem, M., et al: ‘Source and relay power selection using biogeography-based optimization for cognitive radio systems’. IEEE Vehicular Technology Conf., 2011, pp. 1–5.
  33. 33)
      • 15. Hamza, A.S., Hamza, H.S., El-Ghoneimy, M.M.: ‘Spectrum allocation in cognitive radio networks using evolutionary algorithms’, in Venkataraman, H., Muntean, G. (Eds.): ‘Cognitive radio and its application for next generation cellular and wireless networks’, (Springer, Dordrecht, 2012), pp. 259–285.
  34. 34)
      • 40. Akyildiz, I.F., Lee, W.Y., Vuran, M.C., et al: ‘A survey on spectrum management in cognitive radio networks’, IEEE Commun. Mag., 2008, 46, pp. 40–48.
  35. 35)
      • 5. Goratti, L., Baldini, G., Rabbachin, A.: ‘An URN occupancy approach for cognitive radio networks in DTVB white spaces’, Telecommun. Syst., 2014, 56, pp. 229–244.
  36. 36)
      • 4. Wang, W., Wu, K., Luo, H., et al: ‘Sensing error aware delay-optimal channel allocation scheme for cognitive radio networks’, Telecommun. Syst., 2013, 52, pp. 1895–1904.
  37. 37)
      • 20. Silva, M.A.C., Coelho, L.d.S., Freire, R.Z.: ‘Biogeography-based optimization approach based on predator-prey concepts applied to path planning of 3-dof robot manipulator’. IEEE 15th Conf. Emerging Technologies Factory Automation, 2010, pp. 1–8.
  38. 38)
      • 39. Yucek, T., Arslan, H.: ‘A survey of spectrum sensing algorithms for cognitive radio applications’, IEEE Commun. Surv. Tutorials, 2009, 11, pp. 116–130.
  39. 39)
      • 56. Sharaqa, A., Dib, N.: ‘Design of linear and circular antenna arrays using biogeography based optimization’. IEEE Jordan Conf. Applied Electrical Engineering and Computing Technologies, 2011, pp. 1–6.
  40. 40)
      • 47. Chandrasekaran, K., Simon, S.P., Padhy, N.P.: ‘Binary real coded firefly algorithm for solving unit commitment problem’, Inf. Sci., 2013, 249, pp. 67–84.
  41. 41)
      • 3. Sakran, H., Shokair, M.: ‘Hard and softened combination for cooperative spectrum sensing over imperfect channels in cognitive radio networks’, Telecommun. Syst., 2013, 52, pp. 61–71.
  42. 42)
      • 7. Akyildiz, I.F., Lee, W.Y., Vuran, M.C., et al: ‘Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey’, Comput. Netw., 2006, 50, pp. 2127–2159.
  43. 43)
      • 10. Kloeck, C., Jaekel, H., Jondral, F.K.: ‘Dynamic and local combined pricing, allocation and billing system with cognitive radios’. First IEEE Int. Symp. New Frontiers in Dynamic Spectrum Access Networks, 2005, pp. 73–81.
  44. 44)
      • 59. Ma, H., Simon, D., Fei, M., et al: ‘On the equivalences and differences of evolutionary algorithms’, Eng. Appl. Artif. Intell., 2013, 26, pp. 2397–2407.
  45. 45)
      • 30. Saremi, S., Mirjalili, S., Lewis, A.: ‘Biogeography-based optimisation with chaos’, Neural Comput. Appl., 2014, 25, pp. 1077–1097.
  46. 46)
      • 16. Lam, A.Y.S., Li, V.O.K.: ‘Chemical reaction optimization for cognitive radio spectrum allocation’. IEEE Global Telecommunications Conf., 2010, pp. 1–5.
  47. 47)
      • 26. Jamuna, K., Swarup, K.S.: ‘Power system observability using biogeography based optimization’. Int. Conf. Sustainable Energy and Intelligent Systems, 2011, pp. 384–389.
  48. 48)
      • 18. Lam, A.Y.S., Li, V.O.K., Yu, J.J.Q.: ‘Power-controlled cognitive radio spectrum allocation with chemical reaction optimization’, IEEE Trans. Wirel. Commun., 2013, 12, pp. 3180–3190.
  49. 49)
      • 58. Singh, U., Kumar, H., Kamal, T.S.: ‘Design of yagi-uda antenna using biogeography based optimization’, IEEE Trans. Antennas Propag., 2010, 58, pp. 3375–3379.
  50. 50)
      • 53. Ma, H.: ‘An analysis of the equilibrium of migration models for biogeography-based optimization’, Inf. Sci., 2010, 18, pp. 3444–3464.
  51. 51)
      • 13. Zhao, Z., Peng, Z., Zheng, S., et al: ‘Cognitive radio spectrum allocation using evolutionary algorithms’, IEEE Trans. Wirel. Commun., 2009, 8, pp. 4421–4425.
  52. 52)
      • 8. Nie, N., Comaniciu, C.: ‘Adaptive channel allocation spectrum etiquette for cognitive radio networks’. First IEEE Int. Symp. New Frontiers in Dynamic Spectrum Access Networks, 2005, pp. 269–278.
  53. 53)
      • 52. Guo, W., Wang, L., Wu, Q.: ‘An analysis of the migration rates for biogeography-based optimization’, Inf. Sci., 2014, 254, pp. 111–140.
  54. 54)
      • 22. Kankanala, P., Srivastava, S.C., Srivastava, A.K., et al: ‘Optimal control of voltage and power in a multi-zonal MVDC shipboard power system’, IEEE Trans. Power Syst., 2012, 27, pp. 642–650.
  55. 55)
      • 1. Haykin, S.: ‘Cognitive radio: brain-empowered wireless communications’, IEEE J. Sel. Areas Commun., 2005, 23, pp. 201–220.
  56. 56)
      • 55. Kaur, K., Rattan, M., Patterh, M.S.: ‘Biogeography-based optimisation of cognitive radio system’, Int. J. Electron., 2014, 101, pp. 24–36.
  57. 57)
      • 24. Mandal, K.K., Bhattacharya, B., Tudu, B., et al: ‘A novel population-based optimization algorithm for optimal distribution capacitor planning’. Int. Conf. Energy, Automation and Signal, 2011, pp. 1–6.
  58. 58)
      • 43. Dorigo, M., Gambardella, L.M.: ‘Ant colonies for the travelling salesman problem’, BioSystems, 1997, 43, pp. 73–81.
  59. 59)
      • 9. Huang, J., Berry, R.A., Honig, M.L.: ‘Auction-based spectrum sharing’, Mobile Netw. Appl., 2006, 11, pp. 405–418.
  60. 60)
      • 32. Yang, X.-S.: ‘Firefly algorithms for multimodal optimization’ (Springer, Berlin Heidelberg, 2009), pp. 169–178.