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

Optimal Foraging00:48

Optimal Foraging

12.7K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
12.7K
Cluster Sampling Method01:20

Cluster Sampling Method

13.4K
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...
13.4K
Random Sampling Method01:09

Random Sampling Method

13.3K
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...
13.3K
Predator-Prey Interactions02:39

Predator-Prey Interactions

19.8K
Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
19.8K
Parallel Processing01:20

Parallel Processing

405
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
405
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

39.8K
sp3d and sp3d 2 Hybridization
39.8K

You might also read

Related Articles

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

Sort by
Same author

Erratum: Corrigendum: The distribution and behavioral characteristics of plateau pikas (<i>Ochotonacurzoniae</i>). ZooKeys 1059: 157-171. https://doi.org/10.3897/zookeys.1059.63581.

ZooKeys·2022
Same author

The distribution and behavioral characteristics of plateau pikas (<i>Ochotonacurzoniae</i>).

ZooKeys·2021
Same author

A density estimation model of plateau pika (<i>Ochotona curzoniae</i>) supporting camera-monitoring programs.

Ecology and evolution·2021
Same author

Recovery Degree of the Natural Flow Regimes and the Corresponding Economic Costs for Reservoir Operation in Fish Spawning Seasons.

International journal of environmental research and public health·2019
Same author

Mechanical behavior of an individual adherent MLO-Y4 osteocyte under shear flow.

Biomechanics and modeling in mechanobiology·2016
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
See all related articles

Related Experiment Video

Updated: Nov 3, 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

8.3K

A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm

Ying-Hui Jia1, Jun Qiu2,3, Zhuang-Zhuang Ma2

  • 1College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.

Computational Intelligence and Neuroscience
|June 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel crow swarm optimization (CSO) algorithm, merging Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA). The CSO algorithm enhances exploration and exploitation for complex optimization problems.

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.2K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K

Related Experiment Videos

Last Updated: Nov 3, 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

8.3K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.2K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K

Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Swarm intelligence

Background:

  • Balancing exploitation and exploration is crucial for population-based optimization algorithms.
  • Particle Swarm Optimization (PSO) excels at exploitation but lacks exploration.
  • Crow Search Algorithm (CSA) offers simplicity and randomness, aiding exploration.

Purpose of the Study:

  • To develop a hybrid optimization algorithm combining PSO and CSA.
  • To improve the exploration capabilities of existing algorithms.
  • To enhance the performance on complex, high-dimensional optimization tasks.

Main Methods:

  • A new Crow Swarm Optimization (CSO) algorithm was proposed, coupling PSO and CSA.
  • Individuals explore unknown regions guided by random individuals.
  • The CSO algorithm was tested on benchmark functions (unimodal and multimodal) with varying dimensions.

Main Results:

  • The proposed CSO algorithm demonstrated improved optimization efficiency.
  • Enhanced global search ability was observed compared to PSO and CSA.
  • Increased robustness to parameter settings was achieved.

Conclusions:

  • The hybrid CSO algorithm effectively combines the exploitation strengths of PSO and the exploration capabilities of CSA.
  • CSO shows significant performance improvements, particularly for complex, high-dimensional problems.
  • The proposed algorithm offers a superior approach to optimization challenges.