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

Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

693
The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
693
Stability of structures01:14

Stability of structures

263
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
263
Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

552
Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
552
Pole and System Stability01:24

Pole and System Stability

467
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
467
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

329
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
329
Impact of Groups on Groups01:19

Impact of Groups on Groups

26
Social psychologists analyze how groups influence one another, shaping social structures and interactions through both cooperation and competition. These dynamics manifest in various ways, ranging from economic partnerships to intergroup conflicts that shape societal structures and perceptions.Cooperation and Competition in Intergroup RelationsIntergroup relationships vary across contexts, sometimes fostering cooperation and mutual benefit while at other times leading to conflict and...
26

You might also read

Related Articles

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

Sort by
Same author

RETRACTED: Hasanin et al. Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm. <i>Diagnostics</i> 2022, <i>12</i>, 2844.

Diagnostics (Basel, Switzerland)·2025
Same author

LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition.

Mobile networks and applications : MONET·2024
Same author

Editorial: Emerging applications of text analytics and natural language processing in healthcare.

Frontiers in digital health·2023
Same author

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis.

Soft computing·2023
Same author

Automatic approach for mask detection: effective for COVID-19.

Soft computing·2022
Same author

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm.

Diagnostics (Basel, Switzerland)·2022
Same journal

Affine non-negative collaborative representation based pattern classification.

Complex & intelligent systems·2026
Same journal

Predictive maintenance optimization for industrial equipment via reliable prognosis and risk-aware reinforcement learning.

Complex & intelligent systems·2025
Same journal

Verticox+: vertically distributed Cox proportional hazards model with improved privacy guarantees.

Complex & intelligent systems·2025
Same journal

Improving SVM performance through data reduction and misclassification analysis with linear programming.

Complex & intelligent systems·2025
Same journal

Fake news detection based on a hybrid BERT and LightGBM models.

Complex & intelligent systems·2023
Same journal

WRANet: wavelet integrated residual attention U-Net network for medical image segmentation.

Complex & intelligent systems·2023
See all related articles

Related Experiment Video

Updated: Oct 2, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.8K

Stability analysis based parameter tuning of Social Group Optimization.

Junali Jasmine Jena1, Samarendra Chandan Bindu Dash2, Suresh Chandra Satapathy1

  • 1KIIT (Deemed to be University), Bhubaneswar, India.

Complex & Intelligent Systems
|February 28, 2022
PubMed
Summary
This summary is machine-generated.

Social Group Optimization (SGO) stability was analyzed using the Von Neumann method. This research identifies stable parameter ranges for SGO, ensuring reliable convergence in real-world optimization problems.

Keywords:
Evolutionary optimizationParameter tuningSocial Group OptimizationStability analysis

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

13.1K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.2K

Related Experiment Videos

Last Updated: Oct 2, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.8K
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

13.1K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.2K

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Numerical Analysis

Background:

  • Swarm-based optimization algorithms are widely used but their stochastic nature complicates performance analysis.
  • Understanding the stable operating ranges of these algorithms is crucial for consistent real-world application.
  • The Social Group Optimization (SGO) algorithm shows promise, yet its stability characteristics remain unexamined.

Purpose of the Study:

  • To conduct a stability analysis of the Social Group Optimization (SGO) algorithm.
  • To determine the parameter ranges that ensure stable convergence for SGO.
  • To provide insights into the behavioral characteristics of SGO's algorithmic parameters.

Main Methods:

  • The Von Neumann stability analysis approach was employed.
  • Stability analysis was performed on the Social Group Optimization (SGO) algorithm.
  • Simulations were conducted using eight benchmark functions and their variations (shifted and rotated).

Main Results:

  • The study successfully estimated the stable parameter ranges for SGO.
  • Experimental analysis supported the findings, demonstrating improved performance within the identified stable ranges.
  • The stability analysis confirmed that convergence is ensured when SGO operates within these determined ranges.

Conclusions:

  • The Von Neumann stability analysis provides a method to understand and ensure the reliable convergence of SGO.
  • Identifying and utilizing the stable parameter ranges enhances the practical applicability of SGO for complex optimization tasks.
  • This work bridges a gap in the literature by providing the first stability analysis of the SGO algorithm.