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 Small Populations02:04

Conservation of Small Populations

16.6K
Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
16.6K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

61.6K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
61.6K
Genetic Drift03:33

Genetic Drift

42.8K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
42.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

255
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
255
What is Population Genetics?01:25

What is Population Genetics?

64.3K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
64.3K
Conservation of Declining Populations02:07

Conservation of Declining Populations

12.5K
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.
12.5K

You might also read

Related Articles

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

Sort by
Same author

Bis(2,2'-bipyridine-κ(2)N,N')tris-(nitrato-κ(2)O,O')erbium(III).

Acta crystallographica. Section E, Structure reports online·2012
Same author

Trichloridotris{N-[phen-yl(pyridin-2-yl)methyl-idene]hydroxyl-amine-κ(2)N,N'}neodymium(III).

Acta crystallographica. Section E, Structure reports online·2012
Same author

Bis[μ-N-(2-oxidobenzyl-idene)pyridine-2-carbohydrazidato]bis-[chlorido(methanol-κO)erbium(III)].

Acta crystallographica. Section E, Structure reports online·2012
Same author

Contrast enhanced ultrasonography in the diagnosis of IgG4-negative autoimmune pancreatitis: A case report.

Journal of interventional gastroenterology·2012
Same author

Synthesis, characterization and in vitro anti-tumor activities of matrine derivatives.

Bioorganic & medicinal chemistry letters·2012
Same author

Relations between plasma von Willebrand factor or endothelin-1 and restenosis following carotid artery stenting.

Medical principles and practice : international journal of the Kuwait University, Health Science Centre·2012

Related Experiment Video

Updated: Jan 8, 2026

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

13.4K

An improved Grey Wolf Optimizer based on mutation operator, evolutionary population dynamics, and nonlinear

Yufei Zhang1, Tao Li1, Hua Yang2

  • 1School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, China.

Scientific Reports
|December 23, 2025
PubMed
Summary

The novel MENGWO algorithm enhances Grey Wolf Optimizer (GWO) performance by integrating mutation, evolutionary population dynamics, and nonlinear population size reduction. This addresses GWO

Keywords:
Grey wolf optimizerOptimizationReal-world engineering problemsSwarm intelligence

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.1K

Related Experiment Videos

Last Updated: Jan 8, 2026

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

13.4K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.1K

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Standard Grey Wolf Optimizer (GWO) suffers from slow convergence, premature convergence, and an exploration-exploitation imbalance.
  • These limitations hinder GWO's effectiveness in diverse engineering applications and real-world optimization tasks.
  • Addressing these issues is crucial for improving GWO's efficiency and applicability.

Purpose of the Study:

  • To propose a novel variant of the Grey Wolf Optimizer (GWO) named MENGWO.
  • To enhance GWO's exploration and exploitation balance and improve convergence speed.
  • To validate MENGWO's effectiveness on benchmark functions and engineering design problems.

Main Methods:

  • Introduced a mutation operator inspired by Differential Evolution (DE) with adaptive exploration/exploitation switching.
  • Incorporated an enhanced Evolutionary Population Dynamics (EPD) mechanism for repositioning underperforming agents.
  • Implemented a Nonlinear Population Size Reduction (NPSR) strategy to boost computational efficiency.
  • All components feature dynamically adjusted mechanisms based on iteration progression.

Main Results:

  • MENGWO demonstrated superior performance compared to standard GWO, Particle Swarm Optimization (PSO), and other GWO variants on CEC2005 and CEC2022 benchmark functions.
  • The algorithm showed significant improvements on unimodal, multimodal, and fixed-dimensional multimodal functions across low and high dimensions.
  • MENGWO achieved the best performance on 5 out of 7 engineering design problems, indicating strong practical applicability.

Conclusions:

  • The proposed MENGWO algorithm effectively balances exploration and exploitation capabilities.
  • MENGWO significantly enhances optimization performance, addressing key limitations of the standard GWO.
  • The synergistic integration of mutation, EPD, and NPSR strategies makes MENGWO a promising tool for complex engineering applications.