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

56
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...
56
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

74
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
74
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.4K
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...
5.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

513
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
513
Limits to Natural Selection01:38

Limits to Natural Selection

31.3K
Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
31.3K

You might also read

Related Articles

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

Sort by
Same author

Collaborative Diagnosis of Spatiotemporal Faults and Sensor Anomalies in Parabolic Distributed Parameter Systems.

IEEE transactions on cybernetics·2026
Same author

iBT-Net: an incremental broad transformer network for cancer drug response prediction.

Briefings in bioinformatics·2023
Same author

Human-in-the-Loop Reinforcement Learning in Continuous-Action Space.

IEEE transactions on neural networks and learning systems·2023
Same author

Handling Constrained Multiobjective Optimization Problems via Bidirectional Coevolution.

IEEE transactions on cybernetics·2021
Same author

Spatial Decomposition-Based Fault Detection Framework for Parabolic-Distributed Parameter Processes.

IEEE transactions on cybernetics·2021
Same author

Clustering Ensemble Based on Hybrid Multiview Clustering.

IEEE transactions on cybernetics·2020
Same journal

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

IEEE transactions on cybernetics·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Jul 6, 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

13.0K

ATM-R: An Adaptive Tradeoff Model With Reference Points for Constrained Multiobjective Evolutionary Optimization.

Bing-Chuan Wang, Yunchuan Qin, Xian-Bing Meng

    IEEE Transactions on Cybernetics
    |January 8, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the adaptive tradeoff model with reference points (ATM-R) algorithm for constrained multiobjective evolutionary optimization. ATM-R effectively balances feasibility, diversity, and convergence across different evolutionary phases, outperforming existing methods.

    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

    17.1K
    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

    7.5K

    Related Experiment Videos

    Last Updated: Jul 6, 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

    13.0K
    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

    17.1K
    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

    7.5K

    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Constrained multiobjective evolutionary optimization (CMOEO) aims for well-converged and well-distributed feasible solutions.
    • Balancing feasibility, diversity, and convergence is crucial but challenging in CMOEO.
    • Existing single tradeoff models struggle to adapt to varying significance of these elements across evolutionary phases.

    Purpose of the Study:

    • To introduce a novel algorithm, adaptive tradeoff model with reference points (ATM-R), for CMOEO.
    • To address the limitations of single tradeoff models in balancing feasibility, diversity, and convergence.
    • To enhance the performance of evolutionary algorithms in solving constrained multiobjective optimization problems.

    Main Methods:

    • ATM-R employs distinct tradeoff models tailored to different evolutionary phases (infeasible, semi-feasible, feasible).
    • Leverages reference points to guide tradeoff models and a multiphase mating selection strategy.
    • Specifically, it balances diversity and feasibility in the infeasible phase, transitions to diversity and convergence in the semi-feasible phase, and emphasizes both in the feasible phase.

    Main Results:

    • ATM-R demonstrated effectiveness on benchmark test functions and real-world problems.
    • The algorithm consistently showed competitive performance compared to eight state-of-the-art CMOEO algorithms.
    • Systemic experiments validated the algorithm's ability to achieve well-converged and well-distributed feasible solutions.

    Conclusions:

    • ATM-R provides an effective adaptive strategy for CMOEO by dynamically adjusting tradeoff models.
    • The algorithm successfully balances feasibility, diversity, and convergence throughout the optimization process.
    • ATM-R represents a significant advancement in solving complex constrained multiobjective optimization problems.