中文摘要：

为了解决认知无线电系统中最大和网络效益和用户间公平性联合最优化的多目标频谱分配难题，基于量子蜂群理论和膜计算，提出了一种新的离散多目标组合优化算法——膜量子蜂群优化。所提算法在基础膜可以搜索到单个目标的全局最优解，在表层膜获得兼顾网络效益和公平的Pareto前端解。通过膜间的通信规则、量子觅食行为的协同演进和非支配解排序可获得能同时求解单目标和多目标优化问题的多目标优化算法，并与经典的敏感图论着色算法、遗传算法、量子遗传算法和粒子群算法等频谱分配算法在不同的目标函数下进行仿真性能比较。仿真结果表明：在不同网络效益函数下所提的膜量子蜂群频谱分配算法都能够较好地找到单目标最优解，优于经典的频谱分配算法和已有的智能频谱分配算法，还可获得多目标频谱分配的Pareto前端最优解集。

英文摘要：

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 opti-mization, 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 opti-mal 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.