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In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.
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Keywords:
- cognitive radio /
- multi-objective spectrum allocation /
- quantum bee colony optimization /
- membrane computing
[1] Chai Z Y, Chen L, Zhu S F 2012 Acta Phys. Sin. 61 058801 (in Chinese) [柴争义, 陈亮, 朱思峰 2012 61 058801]
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[19] Chai Z Y, Liu F, Zhu S F 2011 Acta Phys. Sin. 60 068803 (in Chinese) [柴争义, 刘芳, 朱思峰 2011 60 068803]
[20] Srinivas N, Kalyanmoy D 1994 Evolut. Comput. 2 221
[21] Deb K, Pratap A, Agarwal S, Meyarivan T 2002 IEEE Trans. Evolut. Comput. 6 182
[22] Gao H Y, Cao J L, Diao M 2013 Int. J. Comput Appl. Technol. 46 244
[23] Păun G, Rozenberg G 2002 Theor. Comput. Sci. 287 73
[24] Păun G 2000 J. Comput. Syst. Sci. 61 108
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[1] Chai Z Y, Chen L, Zhu S F 2012 Acta Phys. Sin. 61 058801 (in Chinese) [柴争义, 陈亮, 朱思峰 2012 61 058801]
[2] Zheng S L, Yang X N 2012 Acta Phys. Sin. 61 148402 (in Chinese) [郑仕链, 杨小牛 2012 61 148402]
[3] Zhao Z J, Xu S Y, Zheng S L, Yang X N 2009 Acta Phys. Sin. 58 5118 (in Chinese) [赵知劲, 徐世宇, 郑仕链, 杨小牛 2009 58 5118]
[4] Haykin S 2005 IEEE J. Select. Areas Commun. 23 201
[5] Liu Y, Peng Q C, Shao H Z, Peng Q H, Wang L 2013 Acta Phys. Sin. 62 078406 (in Chinese) [刘允, 彭启琮, 邵怀宗, 彭启航, 王玲 2013 62 078406]
[6] Tang J, Misra S, Xue G 2008 Computer Networks 52 2148
[7] Chai Z Y, Zheng L P, Zhu S F 2012 Acta Phys. Sin. 61 118801 (in Chinese) [柴争义, 郑丽萍, 朱思峰 2012 61 118801]
[8] Wang Z, Li Y M, Chen B, Zhou T 2013 Acta Phys. Sin. 62 128802 (in Chinese) [汪照, 李有明, 陈斌, 邹婷 2013 62 128802]
[9] Zu Y X, Zhou J 2012 Chin. Phys. B 21 019501
[10] Zu Y X, Zhou J, Zeng C C 2010 Chin. Phys. B 19 119501
[11] Zheng H, Peng C 2005 Proc. IEEE International Conference on Communications (ICC) 5 3132
[12] Peng C, Zheng H, Zhao B Y 2006 ACM Mobile Networks and Applications 11 555
[13] Clancy T C 2009 Annales des Telecommunications Annals of Telecommunications 64 573
[14] Huang J, Berry R A, Honig M L 2006 ACM Mobile Networks and Applications 11 405
[15] Niyato D, Hossain E 2008 IEEE Trans. Wireless Commun. 7 2651
[16] Zhao Z J, Peng Z, Zheng S L, Shang J N 2009 IEEE Trans. Wireless Commun. 8 4421
[17] Thilakawardana D, Moessner K 2008 IET Commun. 2 827
[18] Zhao Z J, Peng Z, Zheng S L, Xu S Y, Lou C Y, Yang X N 2009 Acta Phys. Sin. 58 1358 (in Chinese) [赵知劲, 彭振, 郑仕链, 徐世宇, 楼才义, 杨小牛 2009 58 1358]
[19] Chai Z Y, Liu F, Zhu S F 2011 Acta Phys. Sin. 60 068803 (in Chinese) [柴争义, 刘芳, 朱思峰 2011 60 068803]
[20] Srinivas N, Kalyanmoy D 1994 Evolut. Comput. 2 221
[21] Deb K, Pratap A, Agarwal S, Meyarivan T 2002 IEEE Trans. Evolut. Comput. 6 182
[22] Gao H Y, Cao J L, Diao M 2013 Int. J. Comput Appl. Technol. 46 244
[23] Păun G, Rozenberg G 2002 Theor. Comput. Sci. 287 73
[24] Păun G 2000 J. Comput. Syst. Sci. 61 108
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