Based on artificial bee colony algorithm and quantum computing, a novel quantum-inspired bee colony optimization (QBCO) algorithm was proposed for the discrete combinatorial optimization problems. In QBCO, two new quantum foraging behaviors were used to find the optimal location of nectar by co-evolution of quantum bee colony. The excellent performance of the QBCO algorithm was proved through some classical benchmark functions. At the same time, an assignment method for cognitive radio spectrum allocation based on QBCO was designed. Simulations were conducted to compare this method with genetic algorithm (GA), quantum genetic algorithm (QGA), particle swarm optimization(PSO) and color-sensitive graph coloring (CSGC) using different network utility functions. The simulation results show that our method can find the near-optimal solution and outperforms previous classic spectrum allocation method and intelligence spectrum allocation methods.%基于人工蜂群理论和量子计算,提出一种新的离散组合优化算法——量子蜂群优化算法.该量子蜂群算法使用2种新的量子觅食行为完成整个量子蜂群的协同演进,快速找到最优的蜜源位置,通过对优化函数的测试验证其高效性.以该量子蜂群算法为基础,提出一种认知无线电频谱分配算法,与经典的遗传算法,量子遗传算法和粒子群算法等智能优化算法及敏感图论着色算法在不同的网络效益函数下进行仿真性能比较.仿真结果表明:本文提出的量子蜂群频谱分配算法均能够较好地找到最优解,优于经典的频谱分配算法和已有的智能频谱分配算法.
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