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

doi:10.11772/j.issn.1001-9081.2021020238

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 215-222.DOI: 10.11772/j.issn.1001-9081.2021020238

• Advanced computing • Previous Articles Next Articles

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

Dawei WANG, Xinhao LIU, Zhu LI(), Bin LU, Aixin GUO, Guoqiang CHAI

College of Physics and Information Engineering，Shanxi Normal University，Linfen Shanxi 041004，China

Received:2020-02-09 Revised:2020-04-21 Accepted:2020-04-28 Online:2021-05-08 Published:2022-01-10
Contact: Zhu LI
About author:WANG Dawei， born in 1989， Ph. D.， lecturer. His research interests include information detection， intelligent information processing.
LIU Xinhao， born in 2001. His research interests include cognitive radio， intelligent optimization algorithm.
LI Zhu， born in 1975， M. S.， associate professor. His research interests include sensor network， communication.
LU Bin， born in 1990， Ph. D.， lecturer. His research interests include semiconductor device， integrated circuit.
GUO Aixin， born in 1991， M. S.， teaching assistant. Her research interests include deep learning， cognitive radio.
CHAI Guoqiang， born in 1989， Ph. D. candidate， lecturer. His research interests include deep learning， intelligent computing.
Supported by:
National Natural Science Foundation of China(62004119);Innovation and Entrepreneurship Training Program for College Students of Shanxi Province(2020225)

离散蝠鲼觅食优化算法及在频谱分配中的应用

王大为, 刘新浩, 李竹(), 芦宾, 郭爱心, 柴国强

山西师范大学物理与信息工程学院，山西临汾 041004

通讯作者: 李竹
作者简介:王大为（1989—），男，山西新绛人，讲师，博士，主要研究方向：信息探测、智能信息处理
刘新浩（2001—），男，山东济宁人，主要研究方向：认知无线电、智能优化算法
李竹（1975—），男，山西朔州人，副教授，硕士，主要研究方向：传感器网络、通信
芦宾（1990—），男，山西曲沃人，讲师，博士，主要研究方向：半导体器件、集成电路
郭爱心（1991—），女，山西文水人，助教，硕士，主要研究方向：深度学习、认知无线电
柴国强（1989—），男，山西襄汾人，讲师，博士研究生，主要研究方向：深度学习、智能计算。
基金资助:
国家自然科学基金资助项目(62004119);山西省高等学校大学生创新创业训练计划项目(2020225)

Abstract

Abstract:

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.

Key words: cognitive radio, spectrum allocation, intelligent computing, Manta Ray Foraging Optimization (MRFO) algorithm, network benefit

摘要：

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。

关键词: 认知无线电, 频谱分配, 智能计算, 蝠鲼觅食优化算法, 网络效益

CLC Number:

TN92

Dawei WANG, Xinhao LIU, Zhu LI, Bin LU, Aixin GUO, Guoqiang CHAI. Discrete manta ray foraging optimization algorithm and its application in spectrum allocation[J]. Journal of Computer Applications, 2022, 42(1): 215-222.

王大为, 刘新浩, 李竹, 芦宾, 郭爱心, 柴国强. 离散蝠鲼觅食优化算法及在频谱分配中的应用[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 215-222.

Figures/Tables 11

Tab. 1 Discrete probability analysis of velocity in chain foraging

$x i d (t)$	$x i - 1 d (t)$	$x b e s t d (t)$	$E [v i d (t)]$	$P {S F [v i d (t)] = 1}$
0	0	0	-2	0.12
0	0	1	0	0.50
0	1	0	0	0.50
0	1	1	2	0.88
1	0	0	2	0.88
1	0	1	0	0.50
1	1	0	0	0.50
1	1	1	-2	0.12

Tab. 1 Discrete probability analysis of velocity in chain foraging

$x i d (t)$	$x i - 1 d (t)$	$x b e s t d (t)$	$E [v i d (t)]$	$P {S F [v i d (t)] = 1}$
0	0	0	-2	0.12
0	0	1	0	0.50
0	1	0	0	0.50
0	1	1	2	0.88
1	0	0	2	0.88
1	0	1	0	0.50
1	1	0	0	0.50
1	1	1	-2	0.12

Tab. 2 Discrete probability analysis of velocity in spiral foraging

$x b e s t d (t)$	$x i - 1 d (t)$	$x i d (t)$	$E [v i d (t)]$	$P {S F [v i d (t)] = 1}$
0	0	0	0	0.50
0	0	1	2	0.88
0	1	0	2	0.88
0	1	1	4	0.98
1	0	0	-4	0.02
1	0	1	-2	0.12
1	1	0	-2	0.12
1	1	1	0	0.50

Tab. 2 Discrete probability analysis of velocity in spiral foraging

$x b e s t d (t)$	$x i - 1 d (t)$	$x i d (t)$	$E [v i d (t)]$	$P {S F [v i d (t)] = 1}$
0	0	0	0	0.50
0	0	1	2	0.88
0	1	0	2	0.88
0	1	1	4	0.98
1	0	0	-4	0.02
1	0	1	-2	0.12
1	1	0	-2	0.12
1	1	1	0	0.50

Tab. 3 Discrete probability analysis of velocity in somersault foraging

$x i d (t)$	$x b e s t d (t)$	$E [v i d (t)]$	$P {S F [v i d (t)] = 1}$
0	0	-2	0.12
0	1	2	0.88
1	0	2	0.88
1	1	-2	0.12

Tab. 3 Discrete probability analysis of velocity in somersault foraging

$x i d (t)$	$x b e s t d (t)$	$E [v i d (t)]$	$P {S F [v i d (t)] = 1}$
0	0	-2	0.12
0	1	2	0.88
1	0	2	0.88
1	1	-2	0.12

Fig. 1 Example of search space compression and recovery

Fig. 2 Algorithm convergence process comparison of different parameters

Fig. 3 Robustness comparison of different algorithms

Tab. 4 Mean and standard deviation of different algorithms

算法	均值	标准差
DMRFO	362.60	4.14
IBPSO	349.81	6.77
BPSO	322.50	6.04
DABC	332.86	6.85

Fig. 4 Convergence curves of network total benefit of different algorithms

Fig. 5 Network total benefits of different algorithms in different communication environments

Fig. 6 Average benefit with the number of cognitive users N increasing

Fig. 7 Average benefit with the number of available spectrums M increasing

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

离散蝠鲼觅食优化算法及在频谱分配中的应用

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