用于认知无线电协作频谱感知的混合蛙跳算法群体初始化技术

提出了一种用于认知无线电线性加权协作频谱感知的改进混合蛙跳算法(shuffled frog leaping algorithm, SFLA) 的群体初始化技术, 提出在SFLA初始群体中包含基于修正偏差因子所得的解, 从而改进算法初期性能. 仿真结果表明相比于传统群体初始化技术, 本文所提出的群体初始化技术能够以更快的速率得到期望解, 从而节约计算时间, 更有利于实时应用 关键词： 认知无线电 频谱感知 混合蛙跳算法 群体初始化

Swarm initialization of shuffled frog leaping algorithm for cooperative spectrum sensing in cognitive radio

A swarm initialization method is proposed for modified shuffled frog leaping algorithm (SFLA) for linear combination cooperative spectrum sensing in cognitive radio. The solution obtained by modified deflection coefficient optimization is included in the initial swarm of SFLA, thus improving the performance of the algorithm at the early stage of the search. Simulations show that compared with the traditional swarm initialization technique, the proposed swarm initialization can obtain expected solutions faster, which means that the proposed technique can save computation time and is more suitable for real time applications.