Evidence map›Paper›PMID 39865090›Full record

ArticleScientific reports2025

An improved sparrow search algorithm with multi-strategy integration.

Zongyao Wang, Qiyang Peng, Wei Rao, Dan Li

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zongyao WangSchool of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China.
Qiyang PengSchool of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China.
Wei RaoSchool of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China. joey202411@163.com.
Dan LiKey Laboratory for Information Science of Electromagnetic Waves and the Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.

Funding

National Natural Science Foundation of China 61961025Natural Science Foundation of Jiangxi Province 20202BABL202001
6 · The paper itself

Abstract

Addressing the shortcomings of the Sparrow Search Algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search Algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer's position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the Coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum. Finally, a proposed random opposition-based learning strategy (ROBLS) is applied to the population after each iteration to enhance its diversity. To verify MISSA's effectiveness, extensive testing is conducted on 24 benchmark functions as well as CEC 2017 functions. The experimental results, complemented by Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA and other advanced optimization algorithms, exhibiting superior overall performance.

Indexed as

Black-winged kite algorithmCoot algorithmMulti-strategyOpposition-based learning strategySparrow search algorithm

Identifiers

PMID39865090
PMCPMC11770111

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.