Jove
Visualize
Contact Us

Related Concept Videos

Optimal Foraging00:48

Optimal Foraging

12.7K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
12.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

145
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...
145
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.0K
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...
6.0K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.9K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.9K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.5K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.5K
Equity Theory01:26

Equity Theory

62
Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
62

You might also read

Related Articles

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

Sort by
Same author

Anti-coronavirus optimization algorithm.

Soft computing·2022
Same journal

Battle royale optimizer for multilevel image thresholding.

The Journal of supercomputing·2025
Same journal

MOBRO: multi-objective battle royale optimizer.

The Journal of supercomputing·2025
Same journal

Optimizing inference of segmentation on high-resolution images in MLExchange.

The Journal of supercomputing·2025
Same journal

Topic sentiment analysis based on deep neural network using document embedding technique.

The Journal of supercomputing·2023
Same journal

AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis.

The Journal of supercomputing·2023
Same journal

A Fechner multiscale local descriptor for face recognition.

The Journal of supercomputing·2023
See all related articles
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: Oct 31, 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

Stock exchange trading optimization algorithm: a human-inspired method for global optimization.

Hojjat Emami1

  • 1University of Bonab, Bonab, Iran.

The Journal of Supercomputing
|June 30, 2021
PubMed
Summary
This summary is machine-generated.

A new optimization algorithm, stock exchange trading optimization (SETO), mimics trader behavior to solve complex numerical and engineering problems effectively. SETO demonstrates competitive performance, achieving global optima on most tested functions and engineering challenges.

Keywords:
Engineering design problemsHuman-inspired meta-heuristicNumerical optimizationStock exchange trading optimization (SETO) algorithm

More Related Videos

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.3K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.3K

Related Experiment Videos

Last Updated: Oct 31, 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
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.3K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.3K

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Optimization problems are prevalent in numerical and engineering fields.
  • Existing meta-heuristic algorithms face challenges in complex, high-dimensional problems.
  • Inspiration from real-world systems can lead to novel optimization strategies.

Purpose of the Study:

  • Introduce a novel human-inspired optimization algorithm: Stock Exchange Trading Optimization (SETO).
  • Evaluate SETO's performance on numerical and engineering optimization tasks.
  • Compare SETO against established meta-heuristic optimizers.

Main Methods:

  • Developed SETO based on stock market trading strategies (technical analysis).
  • Incorporated three operators: rising, falling, and exchange, to guide search agents.
  • Tested SETO on 40 single-objective unconstrained numerical functions and 4 engineering design problems.
  • Compared performance against seven popular meta-heuristic algorithms.

Main Results:

  • SETO achieved the global optimum on 36 out of 40 numerical functions.
  • SETO obtained the best results on 3 out of 4 engineering problems.
  • Demonstrated competitive and promising performance, especially on 1000-dimension problems.

Conclusions:

  • SETO is a capable and effective optimization algorithm inspired by financial markets.
  • The algorithm shows strong performance across various problem types and dimensions.
  • SETO offers a promising alternative for solving complex optimization challenges.