基于疯狂自适应鱼群算法的认知无线电频谱分配

针对目前认知无线电网络中频谱利用无法满足指数级增长的通信需求、人工鱼群算法保持种群多样性差、全局搜索能力弱的问题，对图论频谱分配模型的人工鱼群算法进行了改进，得到了网络效益函数最优情况下的频谱分配.首先自适应调整视野和步长，保证算法前期较强的全局搜索能力和后期的收敛精度；然后在随机行为模式下引入疯狂算子，产生扰动以增加种群多样性.仿真实验对比了4种不同算法在相同模型参数下的系统总效益，同时对可用频谱和认知用户分别设置控制变量法测试算法性能.结果表明：改进后的人工鱼群算法全局搜索能力较强，具有较强的鲁棒性.

Cognitive Radio Spectrum Allocation Based on Crazy Adaptive Fish Swarm Algorithm

Facing the facts in cognitive radio networks that spectrum utilization cannot meet the demand of exponentially increasing communication, and artificial fish swarm algorithm maintains poor population diversity and weak global search ability, this paper propose an improved artificial fish swarm algorithm under the graph theory spectrum allocation model to obtain the optimal spectrum allocation under the network benefit function. Firstly, the field of visual and step size are adjusted adaptively to ensure the algorithm having strong global search ability of the algorithm in earlier stages and high convergence accuracy in later stages. Secondly, crazy operators are introduced into the food source to generate disturbances with increased population diversity. By comparing the total benefits of four algorithms in the same spectrum environment, and setting up control variable method separately for the available spectrum and cognitive users, we test the performance of the algorithm. Simulation experiment indicates that the improved artificial fish swarm algorithm has better global search capability and robustness compared with other three algorithms.