基于萤火虫算法的认知车载网络频谱分配

针对布谷鸟搜索算法在认知车载网中频谱分配收敛速度低的问题,提出了一种基于萤火虫算法的频谱分配方法。该方法考虑种群所获得的平均收益值,将频谱分配变量映射为萤火虫位置信息,并将车载网络的吞吐量转化为萤火虫的亮度值,采用萤火虫算法离散频谱分配变量并进行迭代寻优。数值结果表明,基于萤火虫算法的认知车载网络频谱分配方式的收敛速度快,且种群的平均收益值高于遗传算法和布谷鸟算法。

Spectrum Allocation in Cognitive Vehicular Network Based on Firefly Algorithm

To solve slow convergence of cuckoo search algorithm for spectrum allocation in cognitive vehicle network, a new method based on firefly algorithm is proposed. In this method, the average benefit value of population is considered, the spectrum allocation variable is mapped into firefly location information, and the vehicle network throughput is converted into the firefly’s brightness value. Moreover, it utilizes firefly algorithm to discrete spectrum allocation variable and performs iterative optimization. The numerical analysis shows that the spectrum allocation of cognitive vehicle network based on firefly algorithm has a fast convergence speed, and the average benefit value is higher than that of genetic algorithm and cuckoo algorithm.