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

45
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...
45
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

390
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
390
Response Surface Methodology01:16

Response Surface Methodology

98
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
98
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
33
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
What are Estimates?01:06

What are Estimates?

5.0K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
5.0K

You might also read

Related Articles

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

Sort by
Same author

Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach.

PLoS computational biology·2026
Same author

Indium phosphide quantum dots: advanced synthesis, surface engineering, and biomedical applications in imaging, sensing, and therapy.

RSC advances·2026
Same author

OptiSelect and EnShap: Integrating machine learning and game theory for ischemic stroke prediction.

PloS one·2025
Same author

Cellular and Transcriptional Responses of Human Bronchial Epithelial Cells to Delta-9-Tetrahydrocannabinol In Vitro.

International journal of molecular sciences·2025
Same author

DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.

Briefings in bioinformatics·2025
Same author

Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.

Current oncology (Toronto, Ont.)·2024

Related Experiment Video

Updated: Jun 14, 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

12.9K

PERMMA: Enhancing parameter estimation of software reliability growth models: A comparative analysis of metaheuristic

Vishal Pradhan1, Arijit Patra1, Ankush Jain2

  • 1School of Applied Sciences, Kalinga Institute of Industrial Technology, Odisha, India.

Plos One
|September 4, 2024
PubMed
Summary

This study explores metaheuristic optimization for software reliability growth models (SRGMs). The Regenerative Genetic Algorithm (RGA) and Grey-Wolf Optimizer (GWO) show superior parameter estimation capabilities for SRGMs.

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Related Experiment Videos

Last Updated: Jun 14, 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

12.9K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Area of Science:

  • Software Engineering
  • Reliability Engineering
  • Optimization Algorithms

Background:

  • Software reliability growth models (SRGMs) are crucial for assessing software dependability.
  • Traditional parameter estimation methods like Maximum Likelihood Estimation (MLE) and Least Squares Estimation (LSE) have limitations.
  • Metaheuristic optimization algorithms offer advanced solutions for overcoming these limitations in parameter estimation.

Purpose of the Study:

  • To analyze the applicability of metaheuristic algorithms for parameter estimation in SRGMs.
  • To compare the performance of four metaheuristic algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA).
  • To evaluate these algorithms on actual software failure data using established SRGMs.

Main Methods:

  • Four metaheuristic algorithms (GWO, RGA, SCA, GSA) were employed for parameter estimation.
  • Comparative analysis was conducted using four popular SRGMs and three real-world failure datasets.
  • Performance was evaluated based on convergence criteria and R2 distribution.

Main Results:

  • Metaheuristic algorithms produced parameter estimates close to LSE values.
  • RGA and GWO demonstrated superior performance across various real-world failure datasets.
  • RGA showed faster convergence and higher accuracy in locating optimal solutions compared to GWO and other methods.

Conclusions:

  • RGA and GWO are highly suitable for parameter estimation in SRGMs.
  • The Regenerative Genetic Algorithm (RGA) is recommended for its efficiency and accuracy in optimizing SRGM parameters.
  • Metaheuristic approaches provide a robust alternative for enhancing software reliability analysis.