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

Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

723
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
723
Pareto Chart00:52

Pareto Chart

7.1K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.1K
Alternative Sets of Equilibrium Equations01:31

Alternative Sets of Equilibrium Equations

475
When analyzing the behavior of structures, engineers often rely on the concept of equilibrium. This refers to the state where all forces and moments acting on a system balance each other, resulting in no net movement or rotation. In many cases, equilibrium can be described by a set of standard equations. However, in some situations, alternative sets of equilibrium equations must be used to describe the system's behavior accurately.
One example of such a situation can be observed in a...
475
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

314
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
314
Coplanar Forces01:25

Coplanar Forces

4.6K
Consider an object upon which multiple forces are acting. If the lines of action of each force lie within the same plane, the system can be considered coplanar. The Cartesian vector form can be used to resolve each force into its respective components. For a coplanar system, the system will be in equilibrium if each component of the resultant force equals zero and the resultant force on the system is zero. If the sum of the forces is not equal to zero, then the object will not be in equilibrium...
4.6K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

108
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...
108

You might also read

Related Articles

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

Sort by
Same author

Interaction between dynamic reinforcement learning and working memory of pigeon: A comparative modeling study.

The Journal of experimental biology·2026
Same author

Comparison of large language models and expert multidisciplinary team decisions in colorectal cancer.

BMJ health & care informatics·2026
Same author

ASO Visual Abstract: Peritoneal Lavage Cytology Predicts Peritoneal Metastasis and is Associated with Poor Prognosis in Stage II-III Colorectal Cancer Patients.

Annals of surgical oncology·2025
Same author

Peritoneal Lavage Cytology Predicts Peritoneal Metastasis and is Associated with Poor Prognosis in Patients with Stage II-III Colorectal Cancer.

Annals of surgical oncology·2025
Same author

Applicability of cCR assessment criteria in pMMR rectal cancer patients treated with neoadjuvant chemoradiotherapy combined with immunotherapy.

International journal of surgery (London, England)·2025
Same author

Acute effects of optimal power load flywheel half-squat training on lower limb explosive power under different load volumes.

PeerJ·2025
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Sep 27, 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

Utilizing the Relationship Between Unconstrained and Constrained Pareto Fronts for Constrained Multiobjective

Jing Liang, Kangjia Qiao, Kunjie Yu

    IEEE Transactions on Cybernetics
    |April 15, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel constrained multiobjective evolutionary algorithm (CMOEA) that leverages the relationship between constrained and unconstrained Pareto fronts (CPF/UPF). This approach enhances objective information utilization for solving complex optimization problems.

    More Related Videos

    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
    10:58

    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

    Published on: July 25, 2013

    17.2K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.6K

    Related Experiment Videos

    Last Updated: Sep 27, 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
    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
    10:58

    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

    Published on: July 25, 2013

    17.2K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.6K

    Area of Science:

    • Computational intelligence
    • Optimization algorithms
    • Evolutionary computation

    Background:

    • Constrained multiobjective optimization problems (CMOPs) present significant challenges for evolutionary algorithms due to the need to balance multiple objectives and satisfy constraints.
    • Existing algorithms often struggle with effectively integrating constraint handling and objective optimization.
    • Understanding the interplay between the constrained Pareto front (CPF) and unconstrained Pareto front (UPF) is crucial for developing improved CMOP solvers.

    Purpose of the Study:

    • To explore and utilize the relationship between CPF and UPF for solving CMOPs.
    • To develop a novel constrained multiobjective evolutionary algorithm (CMOEA) that effectively balances objectives and constraints.
    • To enhance the utilization efficiency of objective information in evolutionary optimization.

    Main Methods:

    • The proposed method divides the evolutionary process into a learning stage and an evolving stage.
    • During the learning stage, two populations are evolved to approximate the CPF and UPF, respectively, to measure their relationship using feasibility and dominance information.
    • In the evolving stage, specific strategies are employed based on the learned relationship to guide the optimization process.

    Main Results:

    • Comprehensive experiments on 65 benchmark functions and ten real-world CMOPs were conducted.
    • The proposed CMOEA demonstrated superior or highly competitive performance compared to several state-of-the-art algorithms.
    • The effectiveness of using the CPF-UPF relationship to guide objective information utilization was validated.

    Conclusions:

    • The novel CMOEA effectively addresses the challenges of balancing objectives and constraints in CMOPs.
    • Leveraging the relationship between CPF and UPF offers a promising direction for improving evolutionary optimization techniques.
    • The proposed method provides a robust and efficient approach for solving a wide range of CMOPs.