An improved monarch butterfly spectrum allocation algorithm for multi-source data stream in complex electromagnetic environment

In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication. Due to the characteristics of massive, heterogeneous, and multi-source, the generated multi-source data stream brings difficulties to spectru...

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Vydané v:	EURASIP journal on advances in signal processing Ročník 2023; číslo 1; s. 41 - 19
Hlavní autori:	Liu, Yuchao, Cao, Chenggang, Han, Yu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.12.2023 Springer Springer Nature B.V SpringerOpen
Predmet:	Algorithms Cognition Convergence Data transmission Engineering Genetic algorithms Graph theory model Intelligent Mining for Multi-Source Data Stream Mathematical optimization Monarch butterfly Monarch butterfly algorithm Multi-source data stream Nonrenewable resources Particle swarm optimization Quantum Information Technology Resource allocation Signal,Image and Speech Processing Simulated annealing Spectrum allocation Spintronics Multi-source data stream Spectrum allocation Graph theory model Monarch butterfly algorithm
ISSN:	1687-6180, 1687-6172, 1687-6180
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Abstract	In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication. Due to the characteristics of massive, heterogeneous, and multi-source, the generated multi-source data stream brings difficulties to spectrum cognition. As a result, unreasonable spectrum allocation strategy leads to low utilization of spectrum resources. Optimizing spectrum allocation strategy can effectively improve spectrum utilization. Aiming at the problem of trapped local optimum solution in the genetic algorithm (GA) and particle swarm optimization algorithm (PSO), an improved monarch butterfly algorithm is proposed. Firstly, this paper employs the simulated annealing algorithm to select the migration rate, which increases the diversity of monarch butterfly population. Secondly, chaos mapping algorithm is utilized to improve the optimization ability and convergence speed. Finally, in the view of the problem that the monarch butterfly algorithm is easy to fall into the local optimal solution, there is no better way to escape from the local optimal solution. The Wolf pack updating operator is selected to improve the diversity of the population to generate new monarch butterflies. This method updates the population by generating new monarch butterfly individuals, so as to increasing the diversity of the population. The experimental results show that the improved monarch butterfly algorithm outperforms the other two algorithms in terms of convergence speed and system revenue.
AbstractList	In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication. Due to the characteristics of massive, heterogeneous, and multi-source, the generated multi-source data stream brings difficulties to spectrum cognition. As a result, unreasonable spectrum allocation strategy leads to low utilization of spectrum resources. Optimizing spectrum allocation strategy can effectively improve spectrum utilization. Aiming at the problem of trapped local optimum solution in the genetic algorithm (GA) and particle swarm optimization algorithm (PSO), an improved monarch butterfly algorithm is proposed. Firstly, this paper employs the simulated annealing algorithm to select the migration rate, which increases the diversity of monarch butterfly population. Secondly, chaos mapping algorithm is utilized to improve the optimization ability and convergence speed. Finally, in the view of the problem that the monarch butterfly algorithm is easy to fall into the local optimal solution, there is no better way to escape from the local optimal solution. The Wolf pack updating operator is selected to improve the diversity of the population to generate new monarch butterflies. This method updates the population by generating new monarch butterfly individuals, so as to increasing the diversity of the population. The experimental results show that the improved monarch butterfly algorithm outperforms the other two algorithms in terms of convergence speed and system revenue. Abstract In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication. Due to the characteristics of massive, heterogeneous, and multi-source, the generated multi-source data stream brings difficulties to spectrum cognition. As a result, unreasonable spectrum allocation strategy leads to low utilization of spectrum resources. Optimizing spectrum allocation strategy can effectively improve spectrum utilization. Aiming at the problem of trapped local optimum solution in the genetic algorithm (GA) and particle swarm optimization algorithm (PSO), an improved monarch butterfly algorithm is proposed. Firstly, this paper employs the simulated annealing algorithm to select the migration rate, which increases the diversity of monarch butterfly population. Secondly, chaos mapping algorithm is utilized to improve the optimization ability and convergence speed. Finally, in the view of the problem that the monarch butterfly algorithm is easy to fall into the local optimal solution, there is no better way to escape from the local optimal solution. The Wolf pack updating operator is selected to improve the diversity of the population to generate new monarch butterflies. This method updates the population by generating new monarch butterfly individuals, so as to increasing the diversity of the population. The experimental results show that the improved monarch butterfly algorithm outperforms the other two algorithms in terms of convergence speed and system revenue.
ArticleNumber	41
Audience	Academic
Author	Han, Yu Cao, Chenggang Liu, Yuchao
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Snippet	In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication.... Abstract In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to...
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SubjectTerms	Algorithms Cognition Convergence Data transmission Engineering Genetic algorithms Graph theory model Intelligent Mining for Multi-Source Data Stream Mathematical optimization Monarch butterfly Monarch butterfly algorithm Multi-source data stream Nonrenewable resources Particle swarm optimization Quantum Information Technology Resource allocation Signal,Image and Speech Processing Simulated annealing Spectrum allocation Spintronics
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Title	An improved monarch butterfly spectrum allocation algorithm for multi-source data stream in complex electromagnetic environment
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Volume	2023
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