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

122
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...
122
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.9K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.9K
Problem-Solving01:29

Problem-Solving

288
Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
288
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.6K
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

537
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...
537
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

987
The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...
987

You might also read

Related Articles

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

Sort by
Same author

Experimental quantification of influencing parameters on free-flow flushing efficiency in reservoirs.

Scientific reports·2026
Same author

Experimental analysis of flash flood-induced scour on semi-arid hillslopes.

Scientific reports·2026
Same author

New experimental configuration for investigation of debris accumulation effect on local scour at bridge pier and abutment.

Scientific reports·2026
Same author

Impacts of prolonged different social (equality and inequality) conditions on spatial learning and memory deficits, as well as obsessive-compulsive disorder-like behaviors in male rats.

Physiology & behavior·2025
Same author

Effects of chronic empathic stress on synaptic efficacy, as well as short-term and long-term plasticity at the Schaffer collateral/commissural- CA1 synapses in the dorsal hippocampus of rats.

Metabolic brain disease·2024
Same author

Effects of chronic social equality and inequality conditions on passive avoidance memory and PTSD-like behaviors in rats under chronic empathic stress.

The International journal of neuroscience·2024

Related Experiment Video

Updated: Oct 17, 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

13.1K

A new optimization algorithm to solve multi-objective problems.

Mohammad Reza Sharifi1, Saeid Akbarifard2, Kourosh Qaderi3

  • 1Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Scientific Reports
|October 14, 2021
PubMed
Summary

This study introduces the multi-objective moth swarm algorithm (MOMSA) for complex optimization tasks. MOMSA demonstrates superior performance in maintaining solution spread and reliability compared to existing methods.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.8K

Related Experiment Videos

Last Updated: Oct 17, 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

13.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.8K

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Simultaneous optimization of competing objectives necessitates advanced algorithms.
  • Existing multi-objective optimization methods face challenges in capability and solution spread.

Purpose of the Study:

  • To propose the novel multi-objective moth swarm algorithm (MOMSA) for solving multi-objective problems.
  • To enhance synchronization and maintain a good spread of non-dominated solutions.

Main Methods:

  • Developed a new definition for pathfinder moths and moonlight within the swarm algorithm.
  • Employed a crowding-distance mechanism for efficient solution selection.
  • Utilized an archive to store non-dominated solutions for improved exploration.

Main Results:

  • MOMSA demonstrated superior performance across multi-objective benchmark problems (7-30 dimensions).
  • Compared to MOEA/D, PESA-II, and MOALO, MOMSA showed better results in generational distance, spacing, spread, and maximum spread.
  • Achieved competitive CPU time with high-quality results.

Conclusions:

  • The proposed MOMSA is a robust and reliable model for multi-objective optimization.
  • MOMSA offers enhanced capability in handling complex, competing objectives.
  • The algorithm effectively maintains a good spread of non-dominated solutions.