A novel swarm intelligence optimization approach: sparrow search algorithm

By Jiankai Xue, Bo Shen. Systems Science & Control Engineering, 2020

3,325 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/6af974277664126d8e9e47ed2f06eb56724c0a37

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Citations in the corpus

Advances in Sparrow Search Algorithm: A Comprehensive Survey (2022, methodology): “Examples include Farmland Fertility Algorithm [14], African Vultures Optimization Algorithm (AVOA) [15], Starling murmuration optimizer [16], Sparrow Search Algorıthm [17], and Artificial Gorilla Troops Optimizer [18].”
Dung beetle optimizer: a new meta-heuristic algorithm for global optimization (2022, methodology): “The sparrow search algorithm (SSA) has been designed in [23] and a random walk SSA (RWSSA) has been proposed in [34] to optimize the distribution and signal coverage of 5G networks in open-pit mines.”