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

290
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...
290
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

953
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
953
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

547
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
547
Midpoint Rule01:20

Midpoint Rule

7
Approximating areas under curved boundaries is a common problem in applied mathematics, particularly when an exact calculation is difficult or impractical. One effective numerical method for this purpose is the Midpoint Rule, which provides an estimate of the area under a curve by using rectangular approximations over a specified interval.Description of the Midpoint RuleThe Midpoint Rule begins by dividing the given interval into a number of equal subintervals. For each subinterval, the...
7
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
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...
1.1K
Optimization Problems01:26

Optimization Problems

20
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
20

You might also read

Related Articles

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

Sort by
Same author

Anterior cingulate cortex neuron subtypes differentially regulate seizures.

Epilepsia·2026
Same author

Synergistic Modulation of MOF-Derived Cu-Doped Mn<sub>3</sub>O<sub>4</sub> over Porous Al<sub>2</sub>O<sub>3</sub> Ceramics: Toward High-Performance Toluene Catalytic Oxidation.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

A deep learning model based on combining surface and esophageal ECG data for diagnosis of paroxysmal supraventricular tachycardia.

Digital health·2026
Same author

A Near-Infrared-Triggered Luminescence-Activated System for In Vivo Biomacromolecular Tagging and Photocatalytic Crosslinking for Large-Scale Investigation of RNA-Protein Complexes in Living Mice.

Journal of the American Chemical Society·2026
Same author

The exosomal miRNA/FOXN3 axis empowers fibroblasts with tumor-promoting functions via enhanced migration and altered secretome.

Journal of nanobiotechnology·2026
Same author

Synthesis and fungicidal activity of novel 1,1-diaryl-2-azolylethanols with a pyridine moiety.

Molecular diversity·2026
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 16, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

DRIME: A Distributed Data-Guided RIME Algorithm for Numerical Optimization Problems.

Jinghao Yang1, Yuanyuan Shao2, Bin Fu2

  • 1Metropolitan College, Boston University, Boston, MA 02215, USA.

Biomimetics (Basel, Switzerland)
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the distributed data-guided RIME (DRIME) algorithm to improve global exploration and population diversity in optimization. DRIME enhances information exchange and balances exploration/exploitation for superior performance on complex problems.

Keywords:
CEC test suiteRIMEcandidate poolguided learning strategymetaheuristic algorithms

More Related Videos

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

Related Experiment Videos

Last Updated: Jan 16, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Swarm Intelligence

Background:

  • The RIME algorithm suffers from weak global exploration, limited inter-population information exchange, and insufficient population diversity.
  • These limitations hinder its effectiveness in solving complex optimization problems.

Purpose of the Study:

  • To propose a novel distributed data-guided RIME (DRIME) algorithm to overcome the limitations of the original RIME algorithm.
  • To enhance global exploration, information exchange, and population diversity in optimization.

Main Methods:

  • Introduced a data-distribution-driven guided learning strategy to improve inter-population communication and guide population exploitation/exploration.
  • Implemented a soft-rime search phase using weighted averaging to balance development and exploration.
  • Utilized a candidate pool to replace the hard-rime puncture mechanism's optimal reference point, increasing population diversity and reducing local optima risk.

Main Results:

  • DRIME achieved superior performance compared to several state-of-the-art algorithms on the CEC-2017 and CEC-2022 test sets, evidenced by competitive Friedman rankings.
  • Demonstrated excellent performance on engineering constraint optimization problems with an average ranking of 1.23.
  • Parameter sensitivity and strategy effectiveness analyses confirmed the algorithm's robustness and the efficacy of its novel components.

Conclusions:

  • The proposed DRIME algorithm significantly enhances the RIME algorithm's capabilities in global exploration, information exchange, and population diversity.
  • DRIME exhibits strong search capabilities and provides effective solutions for a broad spectrum of optimization challenges.
  • The findings suggest DRIME is a promising advancement in swarm intelligence and optimization algorithm design.