基于混沌进化的引力搜索频谱调度算法

SPECTRUM SCHEDULING ALGORITHM BASED ON CHAOTIC AND EVOLUTIONAL GRAVITATIONAL SEARCH

传统智能寻优算法在解决认知无线电网络中频谱调度问题时,通常会面临初始值敏感、易陷入局部最优、收敛速度慢等问题。针对这些问题,基于引力搜索算法,设计差分进化优化机制用于加快算法的搜索过程,提升收敛速度;设计混沌扰动优化机制,帮助算法跳出局部最优,以提升算法的收敛精度。实验结果表明,相比于传统引力搜索算法和遗传算法,该算法能够以更快的速度达到收敛,且可以获得更高的系统效用。

In solving the problem of spectrum scheduling in cognitive radio networks,traditional intelligent optimization algorithms often face some problems,such as sensitivity to initial values,easy to fall into local optimal,slow convergence and so on.To solve these problems,based on the gravitational search algorithm,the differential evolution optimization mechanism was designed to speed up the search process and improve the convergence speed.And the chaos optimization mechanism was designed to help the algorithm jump out of the local optimal,and improve the convergence precision.The experimental results show that the proposed algorithm can converge at a faster speed compared with the traditional gravitational search algorithm and genetic algorithm,and can obtain higher system utility.