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

9.8K
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.
9.8K
Cluster Sampling Method01:20

Cluster Sampling Method

13.1K
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...
13.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
12.0K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

211
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...
211
Stratified Sampling Method01:16

Stratified Sampling Method

13.2K
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...
13.2K
Random Sampling Method01:09

Random Sampling Method

12.8K
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...
12.8K

You might also read

Related Articles

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

Sort by
Same author

Multi-Agent Reinforcement Learning in Games: Research and Applications.

Biomimetics (Basel, Switzerland)·2025
Same author

FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning.

Biomimetics (Basel, Switzerland)·2025
Same author

A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems.

Biomimetics (Basel, Switzerland)·2024
Same author

SR-DSFF and FENet-ReID: A Two-Stage Approach for Cross Resolution Person Re-Identification.

Computational intelligence and neuroscience·2022
Same author

Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism.

Computational intelligence and neuroscience·2022
Same author

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm.

Computational intelligence and neuroscience·2022

Related Experiment Video

Updated: Oct 8, 2025

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

1.3K

Improved Sparrow Search Algorithm Based on Iterative Local Search.

Shaoqiang Yan1, Ping Yang1, Donglin Zhu2

  • 1Xi'an Research Institute of High Technology, Xi'an, Shaanxi 710025, China.

Computational Intelligence and Neuroscience
|December 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved sparrow search algorithm (ISSA) that enhances search performance and accuracy. The new algorithm effectively avoids local optima and demonstrates strong optimization capabilities in benchmark tests and practical applications.

More Related Videos

A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.5K
Avian Semen Collection by Cloacal Massage and Isolation of DNA from Sperm
07:40

Avian Semen Collection by Cloacal Massage and Isolation of DNA from Sperm

Published on: February 5, 2018

16.4K

Related Experiment Videos

Last Updated: Oct 8, 2025

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

1.3K
A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.5K
Avian Semen Collection by Cloacal Massage and Isolation of DNA from Sperm
07:40

Avian Semen Collection by Cloacal Massage and Isolation of DNA from Sperm

Published on: February 5, 2018

16.4K

Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Metaheuristic Search

Background:

  • The standard sparrow search algorithm (SSA) suffers from poor individual utilization and ineffective search capabilities, leading to local optima and low accuracy.
  • Existing optimization algorithms often struggle with balancing global exploration and local exploitation, hindering performance on complex problems.

Purpose of the Study:

  • To propose an improved sparrow search algorithm (ISSA) that addresses the limitations of the standard SSA.
  • To enhance search performance, accuracy, and the ability to escape local optima.
  • To validate the effectiveness of ISSA on benchmark functions and in practical applications like PID tuning and robot path planning.

Main Methods:

  • Introduced a variable helix factor in the follower's global search phase to improve utilization of opposite solutions and reduce boundary violations.
  • Implemented an improved iterative local search strategy in the follower's local search phase to increase accuracy and prevent optimal solution omission.
  • Incorporated a dimension-by-dimension lens learning strategy for scouters to enhance search flexibility and facilitate escaping local optima.
  • Refined boundary control mechanisms to better utilize out-of-bound individuals while maintaining randomness.

Main Results:

  • ISSA demonstrated superior optimization performance compared to PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic benchmark functions.
  • The algorithm also showed strong performance on the CEC 2017 test functions, indicating good universality even when the optimal solution is not zero.
  • Applied ISSA to PID parameter tuning and robot path planning, yielding effective and practical results.

Conclusions:

  • The proposed ISSA significantly improves upon the standard SSA by enhancing search efficiency, accuracy, and the ability to escape local optima.
  • ISSA exhibits robust performance across various benchmark functions and demonstrates practical applicability in real-world engineering problems.
  • The enhancements contribute to a more versatile and effective metaheuristic optimization algorithm.