离散蝠鲼觅食优化算法及在频谱分配中的应用

Discrete manta ray foraging optimization algorithm and its application in spectrum allocation

针对认知无线电中以最大化网络效益为准则的频谱分配难题以及蝠鲼觅食优化(MRFO)算法难以解决频谱分配问题的不足,提出一种离散蝠鲼觅食优化(DMRFO)算法。根据工程中频谱分配问题具有亲1性的特点,首先,基于Sigmoid函数(SF)离散法对MRFO算法进行离散二进制化;然后,通过异或算子和速度调节因子引导蝠鲼根据当前速度大小自适应向最优解调整下一时刻的位置;同时,通过在全局最优解附近进行二进制螺旋觅食避免算法陷入局部最优;最后,将提出的DMRFO算法应用于解决频谱分配问题。仿真实验结果表明,采用DMRFO算法分配频谱时的网络效益的收敛均值和标准差分别为362.60和4.14,该结果显著优于离散人工蜂群(DABC)算法、二进制粒子群优化(BPSO)算法以及改进的二进制粒子群优化(IBPSO)算法。

Aiming at the problem of spectrum allocation based on maximizing network benefit in cognitive radio and the fact that Manta Ray Foraging Optimization(MRFO)algorithm is difficult to solve the problem of spectrum allocation,a Discrete Manta Ray Foraging Optimization(DMRFO)algorithm was proposed.Considering the pro-1 characteristic of spectrum allocation problem in engineering,firstly,MRFO algorithm was discretely binarized based on the Sigmoid Function(SF)discrete method.Secondly,the XOR operator and velocity adjustment factor were used to guide the manta rays to adaptively adjust the position of next time to the optimal solution according to the current velocity.Then,the binary spiral foraging was carried out near the global optimal solution to avoid the algorithm from falling into the local optimum.Finally,the proposed DMRFO algorithm was applied to solve the spectrum allocation problem.Simulation results show that the convergence mean and standard deviation of the network benefit when using DMRFO algorithm to allocate spectrum are 362.60 and 4.14 respectively,which are significantly better than those of Discrete Artificial Bee Colony(DABC)algorithm,Binary Particle Swarm Optimization(BPSO)algorithm and Improved Binary Particle Swarm Optimization(IBPSO)algorithm.