基于统计特性的蝙蝠算法在认知无线电频谱分配中的应用

针对当前图论频谱分配模型下寻找最优解困难,容易陷入局部最优等问题,将蝙蝠算法引入到认知无线电频谱分配中,并利用图论频谱分配模型的特点,对二进制蝙蝠算法进行改进,以达到更好的效果。首先,将蝙蝠算法的选择策略改为贪婪选择,增强了蝙蝠算法在当前位置的开发能力。其次,统计种群中各蝙蝠经历的最好位置的分布情况,利用蝙蝠位置的统计特性指导蝙蝠寻优,加快算法的收敛速度。最后,在局部搜索时,直接在离散域操作,减少实数到二进制的映射,缩短搜索时间。仿真结果表明,本文算法在效益优于过去的算法的情况下收敛速度更快。

Application of bat algorithm based on statistical characteristics in spectrum allocation of cognitive radio

The bat algorithm is introduced into spectrum allocation to avoid being easily trapped into a local optimization in cognitive radio. The binary bat algorithm is modified based on the spectrum allocation model to achieve better results. Firstly, the selection strategy of the bat algorithm is changed to greedy strategy which can improve the exploitation capability at the current position. Secondly, the distribution of the best position of each bat experienced is analyzed and the statistic characteristic of the bat’s position is used to guide bats finding the optimal solution. It can enhance the speed of the convergence of bat algorithm. Finally, during the local search, the solutions are not obtained by mapping the real number to binary set but being updated directly in the discrete domain, which shortens the searching time. The simulation results show not only that the proposed algorithm is more efficient, but also the speed of convergence is faster than the conventional algorithm.