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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Veröffentlicht in:Systems science & control engineering Jg. 8; H. 1; S. 22 - 34
Hauptverfasser: Xue, Jiankai, Shen, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Macclesfield Taylor & Francis 01.01.2020
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
Algorithms
convergence speed
Optimization
Particle swarm optimization
Search algorithms
sparrow search algorithm
stability and robustness
Swarm intelligence
Swarm optimization
ISSN:2164-2583, 2164-2583
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2164-2583
2164-2583
DOI:10.1080/21642583.2019.1708830