A novel swarm intelligence optimization approach: sparrow search algorithm

In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows. Experiments on 19 benchmark functions are conducted to test the performance of the SSA and its performance is compa...

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Vydáno v:	Systems science & control engineering Ročník 8; číslo 1; s. 22 - 34
Hlavní autoři:	Xue, Jiankai, Shen, Bo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Macclesfield Taylor & Francis 01.01.2020 Taylor & Francis Ltd Taylor & Francis Group
Témata:	Algorithms convergence speed Optimization Particle swarm optimization Search algorithms sparrow search algorithm stability and robustness Swarm intelligence Swarm optimization
ISSN:	2164-2583, 2164-2583
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows. Experiments on 19 benchmark functions are conducted to test the performance of the SSA and its performance is compared with other algorithms such as grey wolf optimizer (GWO), gravitational search algorithm (GSA), and particle swarm optimization (PSO). Simulation results show that the proposed SSA is superior over GWO, PSO and GSA in terms of accuracy, convergence speed, stability and robustness. Finally, the effectiveness of the proposed SSA is demonstrated in two practical engineering examples.
AbstractList	In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows. Experiments on 19 benchmark functions are conducted to test the performance of the SSA and its performance is compared with other algorithms such as grey wolf optimizer (GWO), gravitational search algorithm (GSA), and particle swarm optimization (PSO). Simulation results show that the proposed SSA is superior over GWO, PSO and GSA in terms of accuracy, convergence speed, stability and robustness. Finally, the effectiveness of the proposed SSA is demonstrated in two practical engineering examples.
Author	Xue, Jiankai Shen, Bo
Author_xml	– sequence: 1 givenname: Jiankai surname: Xue fullname: Xue, Jiankai organization: Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education – sequence: 2 givenname: Bo surname: Shen fullname: Shen, Bo email: bo.shen@dhu.edu.cn, shenbodh@gmail.com organization: Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education
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Snippet	In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and...
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StartPage	22
SubjectTerms	Algorithms convergence speed Optimization Particle swarm optimization Search algorithms sparrow search algorithm stability and robustness Swarm intelligence Swarm optimization
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Title	A novel swarm intelligence optimization approach: sparrow search algorithm
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