A novel swarm intelligence optimization approach: sparrow search algorithm

被引:5252
作者
Xue, Jiankai [1 ,2 ]
Shen, Bo [1 ,2 ]
机构
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai, Peoples R China
[2] Minist Educ, Engn Res Ctr Digitalized Text & Fash Technol, Shanghai, Peoples R China
基金
上海市自然科学基金; 中国国家自然科学基金;
关键词
Swarm optimization; sparrow search algorithm; convergence speed; stability and robustness; PRODUCER; COLONY; RISK;
D O I
10.1080/21642583.2019.1708830
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
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.
引用
收藏
页码:22 / 34
页数:13
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