为了有效求解离散优化问题，将量子信息理论引入混合蛙跳算法，提出一种新的组合优化算法——量子蛙跳算法. 量子蛙跳算法使用新的量子跳跃方程完成整个量子蛙群的协同演进，能快速搜索到全局最优位置. 通过对基准函数的测试验证了其高效性，并使用量子蛙跳算法设计了一种认知无线电频谱分配算法. 通过仿真实验对比了所提出的量子蛙跳算法与遗传算法、量子遗传算法、粒子群算法、混合蛙跳算法和敏感图论着色算法等多种算法在不同网络效益函数下实现频谱分配的性能. 在3种网络效益函数下进行的仿真结果表明，所提出的算法能较好地找到最优解，且在不同的网络效益函数下均优于已有的敏感图论着色频谱分配算法和智能频谱分配算法.
To solve a discrete optimization problem, a quantum-inspired shuffled frog leaping (QSFL) algorithm based on shuffled frog leaping algorithm and quantum information theory is proposed. The QSFL algorithm uses quantum movement equations to find the optimal location by the co-evolution of quantum frog colony. Good performance of the QSFL algorithm is shown by some classical benchmark functions. At the same time, we design an assignment method for cognitive radio spectrum allocation without interference based on it. Simulations are conducted to compare this method with genetic algorithm (GA), quantum genetic algorithm (QGA), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA) and color-sensitive graph coloring (CSGC) using different network utility functions. Simulation results indicate that the proposed method can find the near-optimal solution. It outperforms the color-sensitive graph coloring and the previous intelligent spectrum allocation methods.
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