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 Experiment Video

Updated: Aug 16, 2025

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

1.7K

A penalty-based algorithm proposal for engineering optimization problems.

Gulin Zeynep Oztas1, Sabri Erdem2

  • 1Department of Business Administration, Pamukkale University, 20160 Denizli, Turkey.

Neural Computing & Applications
|December 19, 2022
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

95
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...
95
Problem Solving in Statics01:28

Problem Solving in Statics

733
Problem-solving in statics is a crucial aspect of engineering and physics that involves resolving issues associated with bodies in a state of equilibrium. In most cases, problem-solving requires several steps to achieve an accurate result. These steps are crucial to ensuring that the solution is accurate and practical.
The physical situation and mathematical modeling must be considered; however, it is challenging to represent all physical situations using mathematical modeling. With the help of...
733
Bearings: Problem Solving01:24

Bearings: Problem Solving

313
Understanding the calculations and concepts related to double-collar bearings is essential for engineers and designers to optimize the performance of these components in various applications. By analyzing the bearing under different conditions, one can ensure that it can withstand the forces and moments experienced during operation. This knowledge enables better decision-making when designing and selecting bearings for specific purposes and configurations. Consider a double-collar bearing with...
313
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

264
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
264
Bernoulli's Equation: Problem Solving01:16

Bernoulli's Equation: Problem Solving

1.4K
A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
The first step is to compute the cross-sectional areas of the pipe and the Venturi throat to analyze the pressure difference indicated by the pressure gauge. Next, the continuity...
1.4K
Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

1.8K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
1.8K

You might also read

Related Articles

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

Sort by
Same journal

Supporting human-agent communication for explainable planning in spatial-temporal planning problems.

Neural computing & applications·2026
Same journal

Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.

Neural computing & applications·2026
Same journal

Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.

Neural computing & applications·2026
Same journal

Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture.

Neural computing & applications·2025
Same journal

Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization.

Neural computing & applications·2025
Same journal

A fairness scale for real-time recidivism forecasts using a national database of convicted offenders.

Neural computing & applications·2025
See all related articles

This study introduces a novel evolutionary computation model for continuous constrained nonlinear optimization problems. The algorithm enhances solutions through neighbor interactions and a penalty approach, outperforming existing methods on engineering benchmarks.

Area of Science:

  • Optimization
  • Computational Intelligence
  • Engineering Design

Background:

  • Continuous constrained nonlinear optimization problems are challenging.
  • Existing evolutionary algorithms may not effectively handle complex constraints.
  • Novel approaches are needed to improve solution quality and convergence.

Purpose of the Study:

  • To present a population-based evolutionary computation model for continuous constrained nonlinear optimization.
  • To develop an algorithm that achieves superior solutions by incorporating neighbor interactions and advanced memory mechanisms.
  • To address real-world engineering design optimization problems effectively.

Main Methods:

  • A population-based evolutionary computation model with neighbor interaction based on Euclidean distance.
Keywords:
Constrained nonlinear optimizationEngineering benchmark problemsEvolutionary computationMetaheuristicsNatural factsNature-inspired optimization algorithms

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
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

Related Experiment Videos

Last Updated: Aug 16, 2025

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

1.7K
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
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
  • Integration of Tabu Search Algorithm and Elitism for memory usage.
  • Application of a multiplicative penalty approach considering satisfaction rates, constraint deviations, and objective function value.
  • Evaluation on real-world engineering design optimization benchmark problems.

    • Main Results:

    • The proposed algorithm demonstrates satisfactory performance on benchmark problems.
    • Experimental results indicate superior or comparable solutions to existing literature algorithms.
    • The algorithm effectively handles continuous constrained engineering problems.

    Conclusions:

    • The developed evolutionary computation model provides an effective approach for continuous constrained nonlinear optimization.
    • The algorithm's design, combining neighbor interactions and a robust penalty method, leads to improved solution quality.
    • This work offers a valuable tool for solving complex engineering design optimization tasks.