摘要
原始萤火虫(GSO)算法存在收敛速度慢、搜索精度不高等缺点,故设计一种改进型蛙跳萤火虫(FGSO)算法。该算法采用自适应可变步长替换固定步长,并且结合蛙跳算法的族群划分策略,提升萤火虫个体交流能力,实现信息群内共享,以及跳出局部最优的目的。将改进算法应用到认知无线电网络CRN频谱分配问题中,可获取更为优化的频谱分配方案。实验仿真结果表明,从网络效益方面考虑,改进的蛙跳萤火虫算法在总体性能及稳定性方面均优于原始萤火虫算法,并能给出有效的CRN频谱分配策略。
The original glowworm swarm optimization(GSO)algorithm has the problems of slow convergence and low search accuracy Therefore,an improved frog-leaping glowworm swarm optimization(FGSO)algorithm is proposed based on the original algorithm.The algorithm replaces the fixed step size with the adaptive variable step size,and combines the idea of group division of the leapfrog algorithm to improve the individual communication ability of the glowworm.It can realize the sharing of information in the group and jump out of the local optimum.At the same time,the algorithm is used in the spectrum allocation problem of CRN(Cognitive Radio Network)to obtain a more optimized spectrum allocation scheme.The simulation results show that the improved frog-leaping glowworm swarm optimization algorithm is superior to the original algorithm in terms of overall performance and stability,and can provide an effective CRN spectrum allocation scheme.
引文
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