Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Conservation of Declining Populations02:07

Conservation of Declining Populations

9.7K
Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.
9.7K
Cluster Sampling Method01:20

Cluster Sampling Method

12.1K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.1K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

166
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
166
Random Sampling Method01:09

Random Sampling Method

11.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.6K
Stratified Sampling Method01:16

Stratified Sampling Method

12.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data.

PloS one·2026
Same author

Aerial image segmentation using multilevel thresholding based on multi strategy Osprey optimization algorithm.

Scientific reports·2026
Same author

Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection.

Frontiers in artificial intelligence·2026
Same author

The multi-level image segmentation in dermatology application using an enhance Secretary Bird Optimization Algorithm.

Scientific reports·2025
Same author

Memetic Salp Swarm Algorithm for economic load dispatch problems.

Scientific reports·2025
Same author

Deep learning-based feature selection for detection of autism spectrum disorder.

Frontiers in artificial intelligence·2025

Related Experiment Video

Updated: Aug 10, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K

Recent Versions and Applications of Sparrow Search Algorithm.

Mohammed A Awadallah1,2, Mohammed Azmi Al-Betar3,4, Iyad Abu Doush5,6

  • 1Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine.

Archives of Computational Methods in Engineering : State of the Art Reviews
|February 13, 2023
PubMed
Summary

The Sparrow Search Algorithm (SSA), a 2020 swarm-based method inspired by sparrow behavior, is widely applied across engineering and science. This review details its evolution, applications, and future research directions for optimization problems.

More Related Videos

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication
03:53

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication

Published on: November 17, 2023

1.2K
Avian Influenza Surveillance with FTA Cards: Field Methods, Biosafety, and Transportation Issues Solved
12:09

Avian Influenza Surveillance with FTA Cards: Field Methods, Biosafety, and Transportation Issues Solved

Published on: August 2, 2011

19.4K

Related Experiment Videos

Last Updated: Aug 10, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K
Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication
03:53

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication

Published on: November 17, 2023

1.2K
Avian Influenza Surveillance with FTA Cards: Field Methods, Biosafety, and Transportation Issues Solved
12:09

Avian Influenza Surveillance with FTA Cards: Field Methods, Biosafety, and Transportation Issues Solved

Published on: August 2, 2011

19.4K

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • The Sparrow Search Algorithm (SSA), introduced in 2020, is a novel swarm-based optimization technique.
  • SSA's design is inspired by the foraging and anti-predation behaviors observed in sparrows.
  • The algorithm has gained rapid popularity due to its simplicity and effective performance.

Purpose of the Study:

  • To comprehensively review the latest versions and diverse applications of the Sparrow Search Algorithm (SSA).
  • To analyze the growth trajectory, theoretical underpinnings, and key features of SSA.
  • To identify research gaps and suggest future research directions for SSA.

Main Methods:

  • Literature review of published articles on SSA, analyzing publication trends, citations, and covered topics.
  • Examination of extended SSA versions, including modifications and hybridizations aimed at improving convergence and diversity.
  • Review of multi-objective SSA variants designed for complex optimization tasks.

Main Results:

  • SSA has been successfully applied to a wide array of optimization problems in fields like mechanical, electrical, and civil engineering, image processing, and healthcare.
  • Several extended versions of SSA have been developed to address limitations such as premature convergence and enhance solution diversity.
  • The review highlights the algorithm's rapid adoption and its theoretical advancements since its inception.

Conclusions:

  • SSA is a versatile and effective optimization algorithm with significant potential across various scientific and engineering disciplines.
  • Further research is needed to address existing gaps, particularly concerning SSA's convergence behavior.
  • Future work should focus on developing more advanced SSA variants and exploring novel application domains.