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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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...
45
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

636
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
636
Genetic Drift03:33

Genetic Drift

39.6K
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.
39.6K
Differential Leveling01:12

Differential Leveling

144
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
144
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

369
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
369
Hybrid Zones02:29

Hybrid Zones

16.9K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
16.9K

You might also read

Related Articles

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

Sort by
Same author

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Unsupervised fine-tuning of vision-language models by fusing classifier tuning and visual prompt tuning.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

IB2MC: Information Bottleneck Inspired Balanced Multiview Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026

Related Experiment Video

Updated: Jun 15, 2025

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

12.9K

A novel hybrid adaptive differential evolution for global optimization.

Zhiyong Zhang1,2, Jianyong Zhu3, Feiping Nie4

  • 1School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China.

Scientific Reports
|August 24, 2024
PubMed
Summary
This summary is machine-generated.

This study presents an improved Differential Evolution (DE) algorithm with adaptive parameters and dual mutation strategies (APDSDE). APDSDE demonstrates superior performance in global optimization tasks compared to other advanced DE variants.

Keywords:
Adaptive parametersCosine similarityDifferential evolutionDual mutation strategiesPopulation size

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

Related Experiment Videos

Last Updated: Jun 15, 2025

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

12.9K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Engineering Applications

Background:

  • Differential Evolution (DE) is a powerful global optimization algorithm widely used in engineering.
  • The performance of DE is sensitive to its control parameters and mutation strategies.

Purpose of the Study:

  • To introduce a refined DE algorithm, APDSDE, with adaptive parameters and dual mutation strategies.
  • To enhance convergence speed and maintain population diversity in global optimization.

Main Methods:

  • Developed APDSDE with an adaptive switching mechanism between two novel mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1.
  • Introduced a cosine similarity-based parameter adaptation technique for scaling factor and crossover rate weights.
  • Proposed a nonlinear population size reduction method to balance convergence and diversity.

Main Results:

  • APDSDE exhibited superior performance against advanced DE variants on the CEC2017 benchmark functions.
  • The adaptive parameter control and dual mutation strategies significantly improved optimization efficiency.
  • The proposed population size reduction method effectively preserved population diversity.

Conclusions:

  • APDSDE offers a robust and efficient approach to global optimization problems.
  • The adaptive strategies and novel mutation methods represent a significant advancement in DE algorithm design.
  • APDSDE is a promising tool for complex engineering optimization challenges.